134 PyTorch Jobs in India

About Naicos

Naicos, a fast-paced startup, builds AI-native products for algorithmic commerce: the future of how e-commerce runs. Our first products are already live with paying customers, and we are shipping new ones continuously.

Your Role

You will drive the research behind our imaging products, finding approaches to hard, unsolved problems in product and apparel imagery that work at production scale. You will run the experiments, prove what is viable, and hand a working approach to the engineering team.

Who We Are Looking For

•     Total experience: 3 years or more, with a strong research orientation

•     Deep learning frameworks in Python: PyTorch or TensorFlow

•     Image processing in Python: OpenCV, Pillow, scikit-image

•     Working knowledge of diffusion and other image generation models

We are looking for a strong research or research-student profile: someone who investigates, experiments and proves an approach, working closely with the AI Architect. Someone who is driven to build solutions, not just desk research.

AI Skills and Experience

•     Computer vision: classical CV alongside deep learning.

•     Segmentation, image-to-image translation, geometry and lighting; 

•     Generative imaging: diffusion models, conditioning and control, fine-tuning and LoRA,

•     Reads academic papers, judges what is reproducible, and turns one into a working prototype in days

Good to have

•     3D and rendering; published research or open-source contributions; model optimisation for inference cost

Research and innovative problem solving

•     Comfortable where there is no known answer, and defines the approach yourself

•     Solves problems inventively rather than reaching for the biggest model; many results come from classical image processing, fitment and geometric transformation

Other Relevant Skills and Experience

•     Designs experiments: baselines, measurable success criteria, honest reporting of negative results

•     Explains findings to a non-research audience and guides engineers to production

•     Git and reproducible experiment tracking (Weights & Biases, MLflow or similar)

Educational Qualification

•     BE / B.Tech / ME / M.Tech in Computer Science

•     BE / B.Tech / ME / M.Tech in any discipline with proven Computer Vision coursework or work

•     MSc / MS in Computer Science, Maths, Statistics or Computer Vision

•     PhD in Computer Vision or Machine Learning: an advantage, not a requirement

•     Reputed Tier 1 university preferred

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER - COMPANION INTELLIGENCE

Alongside the platform, we are building a proactive personal AI companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders, conversations, and experiences. 

THE OPPORTUNITY

We are looking for a Founding ML Engineer to build the intelligence layer of the platform from the ground up. This is a 0→1 Applied AI / ML role where you will work directly with the founder and Product Engineer to turn ambiguous problems around users, relationships, recommendations and personal intelligence into working systems.

You will be expected to:

Understand the problem → identify the signals → design the intelligence system → prototype → evaluate → deploy → learn → improve.

WHAT YOU WILL BUILD & OWN

USER INTELLIGENCE

User representations, behavioural models, interests, preferences, contextual signals, and evolving understanding of the user. MEMORY Short- and long-term memory, episodic/preference/relationship memory, retrieval, relevance and updating.

RECOMMENDATION & MATCHING

People discovery, compatibility, activity/experience recommendations, and personalized ranking.

INTENT & INTEREST

Infer what the user is trying to do and learn what they care about from behaviour, not only declared interests.

RANKING

Decide what should appear first across potentially thousands of relevant people, activities or experiences.

CONTENT INTELLIGENCE

Classification, toxicity, spam, policy signals, quality, relevance, and semantic understanding.

RELATIONSHIP INTELLIGENCE

Reciprocity, interaction health, shared interests, progression, declining engagement and shared activity.

NEXT-BEST-ACTION

Determine the most useful action now: show a person, suggest a question, recommend an activity, reconnect, or do nothing.

TRUST / SAFETY INTELLIGENCE

Fake-account signals, spam, abuse, behavioural anomalies, risky interactions and moderation assistance.

COMPANION INTELLIGENCE

Use signals and outputs to help the companion decide what to say, suggest, recommend or not do. 

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Translate ambiguous product problems into ML/AI system designs.

• Build models and intelligence pipelines using behavioural, relational and contextual signals.

• Develop recommendation, matching and personalization systems.

• Design memory and retrieval systems that help the companion understand the user over time.

• Build and evaluate LLM-powered and agentic workflows.

• Decide when to use traditional ML, rules, retrieval, ranking or LLMs.

• Prototype quickly, test assumptions and iterate based on real user behaviour.

• Work closely with the founder and Product Engineer to turn intelligence into product experiences.

• Design APIs and production systems that bring ML/AI capabilities into the application.

• Build evaluation, monitoring and feedback loops so the intelligence improves over time.

WHO SHOULD APPLY

• Experience: 0–4 years’ experience, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Strong foundations in ML, Python, statistics and software engineering.

• Evidence of Building: Experience with AI/ML projects, recommendation systems, LLM applications or personalization is highly valued.

• Strong evidence of building: Shipped projects, research, hackathons, internships, open source or startup work. 

WHAT WE LOOK FOR

MACHINE LEARNING DEPTH

Can you understand the modelling problem underneath the application?

RECOMMENDATION & PERSONALIZATION

Can you reason about relevance, ranking, cold start and behavioural signals?

AI ENGINEERING

Can you turn LLMs and agents into reliable product capabilities rather than simple API wrappers?

USER INTELLIGENCE

Can you design systems that gradually understand a person from sparse and changing signals?

SYSTEMS THINKING

Can you move from a model to a production system with APIs, data, latency, cost and monitoring?

EVALUATION MINDSET

Can you determine whether the intelligence actually helped the user?

PRODUCT JUDGMENT

Can you decide what the system should do when there is no predefined answer?

SPEED OF EXECUTION

Can you move from idea → prototype → evaluation → production quickly and responsibly?

BUILD WITH US

You will join at a stage where many of the answers do not exist yet. You will not simply implement a model someone else selected; you will help decide how the product learns to understand people.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With 23,000 customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career; you become part of a team of Egnyters who are doers, thinkers, and collaborators who embrace and live by our values:

Invested Relationships

Fiscal Prudence

Candid Conversations

ABOUT EGNYTE

Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere.

WHAT YOU’LL DO: 

• Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)
• Optimize models for inference via quantization, pruning, and knowledge distillation
• Deploy models to edge devices, mobile, and local servers with strict latency targets
• Build end-to-end MLOps pipelines from data ingestion to deployment
• Monitor model accuracy, latency, and hardware utilization in production
• Evaluate model quality using benchmarking frameworks and custom evaluation suites

YOUR QUALIFICATIONS:

• SLM Development & Fine-tuning: Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
• Model Optimization: Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
• Edge Deployment: Deploy models to edge devices, mobile, and local servers, etc.
• Pipeline Engineering: Build end-to-end MLOps pipelines — from data ingestion to deployment.
• Performance Monitoring: Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

• Deployment experience on edge or mobile environments
• Knowledge of ONNX export and cross-platform inference
• MLOps tooling — experiment tracking, model registries, CI/CD for ML

EQUAL EMPLOYMENT OPPORTUNITY

At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.

Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.

ABOUT

The Persona Labs is building a new kind of social platform focused on something most social products do not explicitly optimize for: helping people become real friends.

We want to help people discover interesting people around them, find meaningful common ground, start low-pressure interactions, continue promising conversations, create shared experiences, and ultimately build real-life friendships.

DISCOVER → CURIOSITY → COMPATIBILITY → INTERACTION → UNDERSTAND → IRL EXPERIENCE → FRIENDSHIP

THE AI LAYER

Alongside the platform, we are building a proactive personal companion that learns about the user and helps them navigate this journey through personalized recommendations, suggestions, reminders and experiences.

THE OPPORTUNITY

We are looking for a Founding Product Engineer to help build the platform from the ground up. This is a 0→1 product engineering role where you work directly with the founder and engineering team to turn ambiguous product problems into working software.

You will be expected to:

Understand the problem → explore solutions → make trade-offs → design → build → ship → measure → improve.

WHAT YOU WILL BUILD & OWN

MOBILE

Onboarding, profiles, discovery, interactions, messaging, activities, and companion experiences.

SOCIAL SYSTEMS

Relationships, social graph, compatibility, and interaction flows.

BACKEND

APIs, services, authentication, notifications, and background processes.

DATA

PostgreSQL models, user/activity data, analytics, and behavioural signals.

REAL-TIME

Messaging, live interactions, notifications, and event-driven workflows.

AI-ENABLED

Recommendations, personalization, and companion-powered experiences.

INFRASTRUCTURE

Cloud deployment, CI/CD, monitoring, reliability, and cost management.

WHAT YOUR DAY-TO-DAY LOOKS LIKE

• Build and ship mobile and full-stack product features end-to-end.

• Design APIs, database models, and backend services for a social product.

• Translate ambiguous product problems into simple, reliable technical solutions.

• Make architecture and engineering trade-offs for performance, scalability, security, and cost.

• Work closely with the founder, product/design team, and Companion Intelligence engineer.

• Prototype quickly, test assumptions, and iterate based on user behaviour and product feedback.

• Own features from problem → architecture → implementation → deployment → improvement.

• Help establish engineering practices around testing, monitoring, security, and reliability.

WHO SHOULD APPLY

• Experience: 0–4 years, including exceptional fresh graduates. Strong 1–3 year engineers and experienced 3–4 year product builders are welcome.

• Fundamentals: Strong CS/software engineering fundamentals.

• Evidence of Building: We value what you have built more than the number of years on your résumé. Shipped apps, strong side projects, hackathons, internships, open source, or startup work are highly valued.

• Mindset: High ownership, curiosity, strong fundamentals, clear communication, learning velocity, and the ability to work through ambiguity.

WHAT WE LOOK FOR

MOBILE CAPABILITY

Can you build and ship a polished mobile experience?

ENGINEERING DEPTH

Do you understand why a system works, not just how to make it run?

PRODUCT JUDGMENT

Can you determine what should be built when the problem is ambiguous?

ARCHITECTURE / SYSTEMS

Can you reason across APIs, databases, services, real-time systems, scale, and cost?

SPEED OF EXECUTION

Can you move from idea → prototype → production quickly without compromising fundamentals?

AI FAMILIARITY

Do you understand how modern AI can become a useful, reliable product capability?

BUILD WITH US

You will join at a stage where many answers do not exist yet. You will not simply implement someone else’s architecture, you will help decide what the architecture and product should become.

CAREERS:

Apply with your resume, GitHub, portfolio or shipped work.

https://forms.gle/12YpUSBY2Sqs5xjp8

www.thepersonalabs.com

This is a remote position.

About Leegality:

Leegality works with large Indian businesses to digitally transform critical compliance processes in a fast, easy and secure way.

We have multiple products across 2 categories:

Document Infrastructure:

Products that help businesses build paperless processes at scale:

1. Document Execution Workflow: A unified platform for businesses to digitally execute (eSign, eStamp, Template Pre-fill, Document Fraud Prevention etc.) agreements, forms and other documents in a compliant way. Currently in use by 2000+ Indian businesses from giants like HDFC and SBI Cards to high-growth disruptors like goDigit and Cars24.
2. Contract Management: An AI-powered platform for businesses to quickly review, negotiate and take action on contract
3. Signstation: A simple platform for businesses to digitally sign simple documents like invoices, policies and letters in a cost effective manner

Consent Infrastructure:

1. Consentin: An end-to-end DPDP and Privacy compliance platform for Indian businesses
2. Consentin Lens: A data discovery platform for businesses to identify the personal data they collect and store.

If you’re interested in building mission critical software that operates at population scale (75 million + Indians have signed at least one document through Leegality) then join Leegality.

Curious about our impact? Explore our customer success stories: leegality.com/case-studies

Our Culture

At Leegality, trust, ownership, transparency, and having fun while doing meaningful work are core to how we operate — not just values on paper. Our team rated us an incredible 97 eNPS for FY 2023–24 — the highest among 175+ startups surveyed.

We focus deeply on helping our people grow and stay motivated. Some of the perks you’ll enjoy:

• Flexible working hours
• Hybrid work setup
• Bi-annual performance appraisals
• A culture that rewards initiative, curiosity, and impact

If you're looking for a place where you can make a real difference while working with smart, driven, and genuinely nice people, welcome to Leegality.

Location: Hybrid

Job Brief:

• As a Machine Learning Engineer specializing in Computer Vision (CV) and Natural Language Processing (NLP), you will develop solutions to interesting technical problems, exploring exciting growth opportunities and having a real impact on our product, particularly focusing on document and content intelligence.
• To ensure success, you should demonstrate solid data science knowledge and experience in a related ML, CV, or NLP role. A first-class engineer will be someone whose expertise enhances our systems for document intelligence and content processing

Responsibilities:

• Designing machine learning systems, self-running artificial intelligence (AI) software, and specialized models for Computer Vision and Natural Language Processing applications.
• Transforming data science prototypes and applying appropriate deep learning algorithms and tools to text and image/document data.
• Solving complex CV and NLP problems with multi-layered data types, such as image/document classification, information extraction, semantic search, and object detection.
• Optimizing existing machine learning models, with a focus on high-performance model deployment for CV and NLP tasks.
• Developing ML algorithms (including large language models/LLMs and computer vision models) to analyze huge volumes of historical text, image, and document data to make predictions and automate workflows.
• Running tests, performing statistical analysis, and interpreting test results for CV/NLP model performance.
• Documenting machine learning processes, model architectures, and data pipelines.
• Keeping abreast of developments in machine learning, Computer Vision, and Natural Language Processing.

Requirements:

• 3+ years of relevant experience in Machine Learning Engineering, with a strong focus on Computer Vision and/or Natural Language Processing.
• Advanced proficiency with Python.
• Extensive knowledge of ML frameworks, libraries (e.g., PyTorch, Transformers), data structures, data modeling, and software architecture.
• Experience with building and maintaining scalable RESTful APIs (e.g., FastAPI).
• In-depth knowledge of mathematics, statistics, deep learning (CNNs, RNNs, Transformers), and algorithms.
• Superb analytical and problem-solving abilities, especially for unstructured data challenges.
• Great communication and collaboration skills.
• Excellent time management and organizational abilities.
• Experience with cloud platforms (e.g., AWS) for model deployment and MLOps.

Recruitment Process:

• Our hiring process combines AI-powered evaluations with structured interviews to ensure a fair and seamless experience.
• You will be contacted via email with the next steps upon being shortlisted.
• The process may include Assessments, AI-enabled interviews, and In-Person Interviews with our team.
• Final selection and CTC will be based on your overall performance and experience.

Apply directly through our career page: https://careers.leegality.com/jobs/Careers

For more information about us please visit our:

Our Company and Culture: https://bit.ly/3Iqm5SB

Our Website: www.leegality.com/

Our LinkedIn Page: www.linkedin.com/company/leegality/

Leegality's Privacy Notice: https://www.leegality.com/employee-privacy-notice

Job Description:

We are seeking a versatile and highly skilled Lead AI/ML Engineer with deep expertise in Generative AI (GenAI) and Large Language Models (LLMs). This role requires a leader who can take full ownership of the

AI lifecycle—from initial architectural design to final production execution. You will lead the development of scalable AI-powered applications, demonstrating exceptional execution skills and the ability to deliver high-performance results under pressure in demanding production environments.

Machine Learning & LLM Capability:

 End-to-End ML Engineering: Build and manage comprehensive ML pipelines, including data ingestion, preprocessing, training, and evaluation using frameworks like PyTorch, TensorFlow, and Scikit-learn.  Advanced LLM Systems: Design and implement sophisticated LLM-based applications such as autonomous agents, chatbots, and complex automation tools.

 Generative AI Specialization: Architect and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases like FAISS, Pinecone, or Weaviate.

 Model Optimization: Fine-tune open-source and proprietary models (e.g., LLaMA, GPT) using advanced techniques like LoRA, QLoRA, or instruction tuning.

 Agentic Frameworks: Develop complex agentic workflows utilizing frameworks such as LangChain or LlamaIndex.

 Prompt Engineering: Implement expert-level prompt engineering, tool/function calling, and structured output generation.

 Project Ownership & Execution

 Full Lifecycle Ownership: Take complete accountability for the full ML and GenAI lifecycle, spanning data processing, model development, monitoring, and optimization.

 Architectural Leadership: Drive strategic architectural decisions for AI platforms, ensuring they are modular, scalable, and maintainable.

 Execution Excellence: Write clean, high-performance Python code following strict OOP principles and manage CI/CD pipelines for seamless project execution.

 Leadership & Mentoring: Act as a key technical leader, managing stakeholders and mentoring team members to ensure all project milestones are met with quality.

 System Integrity: Manage model and prompt versioning, experiment tracking, and comprehensive documentation for all pipelines and workflows.

 Performance Under Pressure

 Production Reliability: Ensure all AI systems maintain extreme scalability and performance under heavy production workloads, including both batch and real-time processing.

 High-Pressure Optimization: Rapidly optimize inference latency and system costs for ML and LLM systems to meet urgent business and technical requirements.

 Proactive Problem Solving: Apply strong analytical thinking to address complex challenges such as system drift, hallucinations, and latency in fast-paced environments.

 Robust Guardrails: Implement and manage strict evaluation frameworks and feedback loops to maintain system quality under stress.

Qualifications:

 Bachelor’s or Master’s degree in Computer Science, AI, ML, or a related field.

 Proven expertise in Python, system design, and scalable AI/ML architecture.

 Deep knowledge of NLP, Computer Vision, and Deep Learning models.

 Hands-on experience with Docker, Kubernetes, MLOps, and major cloud platforms (AWS, GCP, or Azure).

Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have

strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and cloud-based AI deployments.

Key Responsibilities

• Design, develop, and deploy Machine Learning and Deep Learning models for classification, regression, recommendation systems, NLP, Computer Vision, and Generative AI applications.

• Build and maintain end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, evaluation, and deployment.

• Develop AI solutions using PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related frameworks.

• Work with Large Language Models (LLMs) and foundation models such as GPT, BERT, Llama, Claude, and Stable Diffusion.

• Collaborate with product, engineering, and business teams to translate requirements into scalable AI solutions.

• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.

• 7+ years of hands-on experience in AI/ML product development.

• Strong proficiency in Python and ML frameworks including Scikit-learn, TensorFlow, PyTorch, and Hugging Face.

• Experience with Generative AI, LLMs, GANs, VAEs, diffusion models, and prompt engineering.

• Strong understanding of the ML lifecycle including model training, tuning, deployment, monitoring, and optimization.

• Experience with MLOps tools such as MLflow, Docker, Kubeflow, and CI/CD pipelines.

• Experience with AWS, Azure, or GCP cloud platforms.

• Strong problem-solving and analytical skills.

Preferred Skills

• Fine-tuning and deployment of Large Language Models.

• Experience with RAG (Retrieval Augmented Generation) architectures.

• Contributions to open-source AI projects or research publications.

• Knowledge of model interpretability, data annotation, and feature engineering.

• C++ experience for high-performance AI applications.

Why Join Kody Technolab Limited?

Opportunity to work on innovative AI products, Generative AI solutions, robotics integrations,

and enterprise-scale applications while collaborating with a highly skilled technology team.

Visit the Website to know more about us.

Company Website - Kody Technolab | Deep Tech Company in Robotics & AI Solution

Kody Robots | Robotics Company in India for Autonomous Robots

PRINCIPAL AI ENGINEER @ METADOME.AI

Company Description

Metadome.ai builds frontier AI models that transform text, drawings, and CAD into production-ready, interactive 3D experiences. The company advances a full generative pipeline—text-to-CAD, 2D-to-3D

reconstruction, CAD completion and harmonization, and real-time interactive rendering—engineered for the precision required in the physical world. Its technology currently powers the modernization of

OEM aftersales for more than 30 automotive and heavy-equipment manufacturers worldwide, delivering accurate, scalable, and fast 3D solutions. Metadome.ai’s platform enables shoppable 3D parts, step-by-step repair animations, and a headless API that feeds consistent 3D assets into commerce, dealer, training, and service systems. The broader mission is to allow anyone to move from an idea, drawing, or specification to a production-grade 3D model and beyond in seconds.

Role Description

As a Principal AI Engineer — Generative CAD & 3D, you will lead the design, development, and deployment of advanced AI models that convert text, 2D drawings, and CAD files into engineering-grade 3D content. You will architect end-to-end generative pipelines, including

text-to-CAD, 2D-to-3D reconstruction, CAD completion, and real-time rendering, collaborating closely with product, design, and engineering teams to ship robust production systems. Day-to-day, you will experiment with novel neural network architectures, optimize model performance on large-scale CAD datasets, write high-quality production code, and guide the integration of AI services into customer-facing platforms. You will mentor other engineers, establish best practices for AI development, and contribute to technical strategy and roadmap. This is a full-time, hybrid role based in Bengaluru, with a mix of on-site collaboration and work-from-home flexibility.

Qualifications

• Strong foundation in Computer Science and Software Development, including data structures, algorithms, system design, and production-grade coding in languages such as Python, C++, or similar.
• Deep expertise in Neural Networks and Pattern Recognition, with hands-on experience designing, training, and deploying modern deep learning architectures for complex, high-dimensional data.
• Experience with Natural Language Processing (NLP), including working with text encoders, multimodal models, and integrating language understanding into generative workflows.
• Advanced degree (Master’s or PhD) in Computer Science, Electrical Engineering, Applied Mathematics, or a related field, or equivalent practical experience in AI/ML research and engineering.
• Background in 3D geometry, CAD, computer graphics, or related domains, with familiarity in 3D representations, mesh processing, and rendering pipelines.

About NonStop io Technologies

NonStop io Technologies is a value-driven company with a strong focus on process-oriented software engineering. We specialize in Product Development and have a decade's worth of experience in building web and mobile applications across various domains. NonStop io Technologies follows core principles that guide its operations and believes in staying invested in a product's vision for the long term. We are a small but proud group of individuals who believe in the 'givers gain' philosophy and strive to provide value in order to seek value. We are committed to and specialize in building cutting-edge technology products and serving as trusted technology partners for startups and enterprises. We pride ourselves on fostering innovation, learning, and community engagement. Join us to work on impactful projects in a collaborative and vibrant environment.

Brief Description:

We're seeking an AI/ML Engineer to join our team. As AI/ML Engineer, you will be responsible for designing, developing, and implementing artificial intelligence (AI) and machine learning (ML) solutions to solve real-world business problems. You will work closely with engineering teams, including software engineers, domain experts, and product managers, to deploy and integrate Applied AI/ML solutions into the products that are being built at NonStop io. Your role will involve researching cutting-edge algorithms and data processing techniques, and implementing scalable solutions to drive innovation and improve the overall user experience.

Responsibilities

● Applied AI/ML engineering; Building engineering solutions on top of the AI/ML tooling available in the industry today. Eg: Engineering APIs around OpenAI

● AI/ML Model Development: Design, develop, and implement machine learning models and algorithms that address specific business challenges, such as natural language processing, computer vision, recommendation systems, anomaly detection, etc.

● Data Preprocessing and Feature Engineering: Cleanse, preprocess, and transform raw data into suitable formats for training and testing AI/ML models. Perform feature engineering to extract relevant features from the data

● Model Training and Evaluation: Train and validate AI/ML models using diverse datasets to achieve optimal performance. Employ appropriate evaluation metrics to assess model accuracy, precision, recall, and other relevant metrics

● Data Visualization: Create clear and insightful data visualizations to aid in understanding data patterns, model behaviour, and performance metrics

● Deployment and Integration: Collaborate with software engineers and DevOps teams to deploy AI/ML models into production environments and integrate them into various applications and systems

● Data Security and Privacy: Ensure compliance with data privacy regulations and implement security measures to protect sensitive information used in AI/ML processes

● Continuous Learning: Stay updated with the latest advancements in AI/ML research, tools, and technologies, and apply them to improve existing models and develop novel solutions

● Documentation: Maintain detailed documentation of the AI/ML development process, including code, models, algorithms, and methodologies for easy understanding and future reference.

Qualifications & Skills

● Bachelor's, Master's, or PhD in Computer Science, Data Science, Machine Learning, or a related field. Advanced degrees or certifications in AI/ML are a plus

● Proven experience as an AI/ML Engineer, Data Scientist, or related role, ideally with a strong portfolio of AI/ML projects

● Proficiency in programming languages commonly used for AI/ML. Preferably Python

● Familiarity with popular AI/ML libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, etc.

● Familiarity with popular AI/ML Models such as GPT3, GPT4, Llama2, BERT etc.

● Strong understanding of machine learning algorithms, statistics, and data structures

● Experience with data preprocessing, data wrangling, and feature engineering

● Knowledge of deep learning architectures, neural networks, and transfer learning

● Familiarity with cloud platforms and services (e.g., AWS, Azure, Google Cloud) for scalable AI/ML deployment

● Solid understanding of software engineering principles and best practices for writing maintainable and scalable code

● Excellent analytical and problem-solving skills, with the ability to think critically and propose innovative solutions

● Effective communication skills to collaborate with cross-functional teams and present complex technical concepts to non-technical stakeholders

We are seeking a Senior Data Science & ML Associate with 4+ years of applied ML experience to build and ship models end-to-end from data prep and feature engineering to training, evaluation, and deployment driving measurable business impact.

Key Responsibilities

• Build, train, and evaluate ML and deep-learning models

• Engineer features and prepare data at scale

• Deploy models and monitor production performance

• Partner with stakeholders to frame problems and metrics

• Communicate results and drive decisions

• Iterate on models from business feedback

Mandatory Skills

• 4+ years applied machine learning

• Strong Python (Pandas, NumPy, scikit-learn)

• Classical ML and deep learning (TensorFlow/PyTorch)

• Solid statistics and experiment design

• SQL and data wrangling at scale

• Model deployment / MLOps exposure

Nice to Have: NLP or computer vision; cloud ML (SageMaker, Azure ML)

Job Description:

We are looking for a skilled Python Developer with 2+ years of hands-on experience in backend development and data processing. The ideal candidate should be proficient in Python and have working experience with web frameworks like Flask and FastAPI, along with exposure to data engineering and machine learning workflows.

Key Responsibilities:

Design, develop, and maintain scalable Python applications and APIs using Flask and FastAPI

Work with PySpark and Kafka for real-time data processing

Perform data manipulation and analysis using Pandas and NumPy

Develop and maintain modular, reusable, and testable code following OOP principles

Collaborate with data scientists to integrate ML models (TensorFlow, PyTorch, Scikit-learn) into production

Participate in code reviews, design discussions, and contribute to best practices

Write and maintain documentation for developed modules and workflows

Required Skills:

Strong proficiency in Python programming

Hands-on experience with Flask and/or FastAPI

Experience working with PySpark and Kafka

Solid understanding of Pandas, NumPy, and data handling in Python

Familiarity with TensorFlow, PyTorch, and scikit-learn

Good grasp of Object-Oriented Programming (OOP) concepts

Ability to write modular and maintainable code

Nice to Have:

Understanding of Machine Learning concepts and pipelines

Exposure to Generative AI (GenAI) technologies and tools

Experience with CI/CD tools, Docker, or cloud environments (AWS, GCP, etc.)

Qualifications:

Bachelor's degree in Computer Science, Engineering, or a related field

• We are looking for experienced AI/ML Architects to join our AI Engineering service line. In this role, you will anchor the technical delivery of enterprise AI/Agentic AI projects post deal-closure. You will take over from solution architects and lead the design, build, deployment, and optimization of AI/ML systems — ensuring production-grade quality, scalability, and compliance. 
• You will interface with cross-functional teams, manage engineering complexity, and ensure value realization for customers across industries such as BFSI, HLS, Manufacturing, CMT, Retail, and Energy. 

Key Responsibilities

Architecture & Technical Leadership

Hands-on Engineering & Problem Solving

Required Qualifications

Education : B.Tech/M.Tech or equivalent in Computer Science, Data Science, or a related field.

Experience

● 10+ years in software architecture or engineering with 5+ years in applied AI/ML

system delivery.

● Experience in productionizing AI/ML models and building full-stack AI applications in

enterprise settings.

● Strong Python development skills; proficiency in ML/AI frameworks (PyTorch,

TensorFlow, Scikit-learn).

● Strong understanding of LLMs, RAG pipelines, vector databases (Weaviate, Qdrant,

Pinecone).

● Experience with MLOps/LLMOps tools: MLflow, Argo, KServe, Feast, Kubeflow.

● Proficiency in data pipeline engineering using Spark, Airflow, or DataFlow.

● Exposure to agent orchestration frameworks: LangChain, LangGraph, AutoGen,

CrewAI is a big plus.

● Cloud & Infrastructure

● Hands-on experience with GCP (Vertex AI, BigQuery, Document AI, AI Gateway)

and/or Azure (Azure ML, OpenAI, Synapse).

● Expertise in containerization (Docker) and orchestration (Kubernetes).

● Familiarity with Infrastructure as Code (Terraform, Pulumi, CDK).

Soft Skills

Strong architectural thinking and problem-solving in fast-paced delivery environments.

Excellent communication and collaboration skills to work across cross-functional teams and

clients.

Proactive, structured, and detail-oriented with a bias for execution.

Nice to Have

Experience in real-world deployments of Agentic AI systems or collaborative multi-agent setups.

Exposure to regulatory/ethical concerns in AI such as fairness, transparency, or bias mitigation.

Familiarity with AI observability, explainability, and governance tooling (e.g., Arize, Fiddler,

TruEra).

About the Role

We’re building the next generation of AI-powered business software, and we’re looking for people who want to shape that future with us. With Lumen, we’re reimagining how users interact with CRM — moving beyond screens, menus and dashboards to an intelligent interface where users can simply ask AI to take actions, retrieve knowledge, generate insights and get work done. With Agent Studio, we’re enabling businesses to build, test and deploy their own AI agents for real-world workflows. And with Invorto, we’re bringing AI to voice, allowing businesses to create intelligent voice agents tailored to their customer and operational use cases.

What makes this especially exciting is the stage and scale of the opportunity. You’ll get to work on genuinely hard problems across LLMs, agents, reasoning, orchestration, voice AI, evaluation, reliability and enterprise security — not as isolated experiments, but as products used in real business workflows. You’ll have the opportunity to build zero-to-one, own meaningful parts of the product end-to-end, work closely with customers, experiment rapidly, and see your work reach production at scale.

Why join now? Because the playbook for enterprise AI is still being written. You won’t just be implementing someone else’s roadmap — you’ll help define the product, architecture and experiences that become that playbook. Expect high ownership, fast iteration, hard technical and product problems, direct customer impact, and the chance to build AI systems that have to work reliably in the real world — not just in a demo.

About the Role

We are looking for a Senior AI/ML Backend Engineer to help build the core intelligence layer powering Lumen and Agent Studio. You will design and ship production-grade backend systems that integrate LLMs into real agentic workflows — taking actions, retrieving knowledge and generating insights inside a live CRM product used by real businesses. This is a hands-on, build-focused role with direct ownership of systems that ship to production.

What You’ll Do

• Design, build and scale backend services in Python that power LLM-driven and agentic features within Lumen and Agent Studio.
• Build and productionize agentic AI systems — including planning, tool use, orchestration, memory and multi-step task execution.
• Integrate LLMs into core product workflows, focusing on reliability, latency, cost and correctness at production scale.
• Build robust APIs and services that connect AI agents with CRM data, business logic and third-party systems.
• Own evaluation, testing and monitoring for AI features to ensure they behave reliably in real-world, not just demo, conditions.
• Collaborate closely with product, design and other engineers to take features from zero to one and iterate rapidly based on real usage and customer feedback.
• Work directly with customers and customer-facing teams to understand real workflows, debug issues and translate feedback into product and engineering decisions.

What We’re Looking For

• 2–4 years of professional backend engineering experience, with strong hands-on Python skills.
• Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
• Should be hands-on with traditional Machine learning frameworks like Pytorch, Scikit-learn
• Solid understanding of API design, backend architecture, databases and distributed systems fundamentals.
• Familiarity with LLM orchestration concepts — prompting, tool/function calling, RAG, agent frameworks, evaluation and guardrails.
• Comfort working in a fast-paced, ambiguous, zero-to-one environment where you’ll be defining as much as building.
• Strong communication skills — this is a customer-facing role, and you will be expected to clearly articulate technical concepts, decisions and trade-offs to both technical and non-technical stakeholders, including customers.

Good to Have

• Experience with enterprise security, reliability or observability practices for AI systems.
• Prior experience working on CRM, SaaS or other enterprise business software.
• Exposure to voice AI or real-time systems.

Are you looking to work in the cutting edge area of applying data-science to help global customers get a better insight into their health? If so, read on and apply.

Role Name: Senior Data Scientist

Science Team | Full-Time | In-Office | Bangalore

The Role

The Ultrahuman Science Team builds the algorithms behind the Ring, M1 CGM, blood and urine biomarkers, and Performance Lab assessments. We are hiring a Senior Data Scientist to own those algorithms end to end: from the raw sensor signal to a model that is shipped, monitored, and trusted in users' hands.

This is a build role with real scope. In a typical month you will improve a production algorithm, root-cause a metric users are complaining about, and stand up the data pipeline the next model needs. The common thread is ownership: you take a vague question and return a working answer, without waiting to be handed scope.

What You'll Do

·      Own algorithms end to end: sleep staging, activity detection, sensor-derived metrics, and health scores. You frame the problem, build the features, train and evaluate the model, and see it live

·      Ship models, not notebooks: you prove a change on our own cohort before it reaches users, and a model is done only when it runs in production and you can tell how it is behaving

·      Validate against reference standards: design evaluations against gold standards, reference devices, and study ground truth, and know when a result is real and when it is an artifact

·      Own the data layer: cohort extraction, feature pipelines, study data, and raw sensor data, so the next model starts from clean inputs

What This Looks Like in Practice

1. Improving production algorithms - Take an existing production model like sleep staging, root-cause the failure modes against reference data, and ship a fix you can defend with numbers.

2. Building new models - Train an activity classifier on raw sensor data, design the labeled data collection that expands it, and pick the operating point so false positives never erode trust.

3. Proving it before it ships - Run a new steps algorithm against reference-device cohorts, decide with data when it is ready, and monitor how it behaves after rollout.

Who You Are

The two things we can't coach

·      High ownership, end to end: you take a problem from a vague question to a shipped model without waiting to be handed scope, and you can point to something you owned from raw data all the way to production

·      Hungry for more scope: you have outgrown your current role and want problems biggerthan your title, with the technical depth to be trusted with them

Also important

·      You've worked with human health data: wearables, physiological signals, or clinical data.

If your experience is close but not exact, show us why you will ramp fast

·      You've built at a startup: or somewhere small enough that nobody handed you clean data, clear specs, or a mature ML platform

·      You work like it's 2026: coding agents and AI tooling are part of how you build every day, and you can tell which new capabilities are worth adopting

·      You communicate: you can explain a model and its limits to a product manager, an engineer, or a founder, and hold your own with our scientists Core Technical Skills

·      Languages and data: Python and SQL daily, comfortable working in a real codebase

·      Machine learning: PyTorch or TensorFlow, scikit-learn, and gradient boosting, with the judgment to know which the problem needs

·      Advanced machine learning: time series and sequence models, deep learning on continuous physiological signals, and ensembles

·      Statistics and evaluation: hypothesis testing, experiment and A/B design, model evaluation, and error analysis against a reference standard

·      Scale and cloud: Spark or equivalent on large datasets, and AWS, GCP, or Azure

·      Production ML and MLOps: training pipelines, model versioning, deployment, monitoring, and drift detection

·      LLMs and agentic systems: fine-tuning and serving models, building agentic pipelines, and using coding agents to move faster

Experience:

- 4 to 5 years building and shipping machine learning systems. We index on what you have shipped and on trajectory, not the exact number of years; if you are a little earlier but have clearly outgrown your current scope, we want to hear from you.

- Bachelor's or higher in engineering, computer science, statistics, or a related field.

How We Work and Who Thrives Here

- The Science team is small and moves fast, and much of the work has no precedent to copy.

- People do their best work here when they are energized by ambiguity, low on ego, quick to adopt a better idea no matter where it comes from, and comfortable owning something before anyone has told them how. If you need a mature data org, clean labelled datasets, and clear guardrails to thrive, this particular role will not be the right fit, and that is worth knowing up front.

What You'll Gain

·      Ownership of algorithms that hundreds of thousands of people see every morning

·      A dataset most scientists never get to touch: 100M+ nights of sleep and continuous physiological signals at scale

·      Direct collaboration with the engineering, product, and design teams building Ultrahuman

Position Overview 

The AI Observability Engineer will be instrumental in implementation of scalable, cloud-native solutions to meet the growing needs of our Data & Development team. The successful candidate will demonstrate the ability to abstract complexity and create reusable, scalable patterns that accelerate development. The AI Observability Engineer will build and maintain a robust framework to ensure the reliability and maintainability of DPR Construction's complex AI systems. 

Responsibilities 

• Standardize observability practices across AI/ML and other development teams including logging, metrics, tracing, and model performance monitoring, ingesting data from multiple platforms 
• Lead hands-on implementation of automation-first DevOps and MLOps practices, enabling infrastructure-as-code and consistent, repeatable environment provisioning 
• Design and manage intelligent DataOps pipelines with automated data quality monitoring and anomaly detection 
• Deploy, maintain and monitor containerized ML workloads 
• Extend existing CI/CD pipelines to support automated infrastructure changes and ML workflows 
• Implement AI-driven data validation, schema and concept drift detection and metadata management. 
• Establish governance frameworks for AI systems, including bias detection, explainability, and auditability 
• Extend existing Azure RBAC strategy by automating role and permission management to reduce manual intervention 
• Develop automated test suites for model performance, regression, edge cases and bias validation 
• Monitor model KPIs (accuracy, precision, recall, latency, calibration) 
• Ensure reproducability of experiments and production models 
• Act as a technical point of contact for DevOps and MLOps practices, developing reusable patterns, documentation, and proof-of-concepts to drive adoption 

Qualifications 

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field 
• 5+ years of experience in DevOps, MLOps, Data Engineering, Software Engineering or Site Reliability Engineering 
• Strong understanding of cloud infrastructure and experience working with at least one major cloud provider, preferably Azure 
• Proficiency in at least one objected-oriented programming language, preferably python with hands-on experience in ml frameworks like TensorFlow, PyTorch or Scikit-learn 

ML Leads JD

Key Responsibilities

• Model Training & Fine-Tuning: Build, fine-tune, and optimize state-of-the-art NLP, LLM, Speech, and Vision models for scheduled Indian languages, utilizing parameter-efficient methods (LoRA, QLoRA, PEFT).
• Indic Tokenization & Linguistics: Architect custom tokenizers and text-normalization pipelines to address the "fertility problem" in Devanagari, Dravidian, and other regional scripts, ensuring low-latency and cost-effective model inference.
• Multimodal System Design: Develop robust OCR engines capable of parsing complex script geometries (conjoint consonants, Shirorekha, vowel modifiers) and integrate them into document intelligence pipelines.
• Speech Engineering: Deploy and scale robust STT (Speech-to-Text) and TTS (Text-to-Speech) pipelines capable of handling heavy code-mixing (e.g., Hinglish, Tanglish), regional accents, and localized dialects.
• Vernacular Guardrails & Evaluation: Establish culturally contextual benchmark datasets and implement safety guardrails.
• Production Deployment (MLOps): Package and serve models using high-throughput frameworks (vLLM, Triton, ONNX) optimized for GPU environments, minimizing computational overhead for massive cross-lingual workloads.
• Vernacular Fraud & Anomaly Detection: Architect risk-scoring systems and anomaly detection models capable of identifying fraud patterns in native scripts and code-mixed formats.

Essential Qualifications & Technical Skills

• Education: Bachelor’s or Master's degree in Computer Science, Mathematics, Statistics, or a closely related quantitative field.
• Experience: 4+ years of professional experience building and deploying machine learning models in production environments, with a proven track record in Indian Language NLP, Speech, or Anomaly Detection.
• Programming: Expert-level proficiency in Python and standard ML frameworks (PyTorch, TensorFlow).
• Indic AI Stack: Direct, hands-on experience with specialized Indic frameworks and datasets (e.g., AI4Bharat's IndicTrans2/IndicWhisper, Bhashini API, Kathbath, Sarvam-105B, or Aksharantar).
• Fraud Stack: Proficiency in tabular/graph-based ML toolkits (XGBoost, LightGBM, PyTorch Geometric) and handling highly imbalanced target variables (SMOTE, class weights).
• NLP & LLMs: Deep understanding of Transformer architectures, sequence-to-sequence modeling, cross-lingual embeddings, vector databases (Milvus, Pinecone, Qdrant), and quantization tools (bitsandbytes, GPTQ).
• Speech & Vision Processing: Experience processing raw audio signals (grapheme-to-phoneme conversion, spectrogram analysis) or document structures using OCR networks (CRAFT, DBNet, LayoutLM).
• Handling Code-Mixing: Proven ability to build models that gracefully parse text or speech containing heavy code-switching (mixed Latin/regional scripts, multi-language grammar).

Job Title : MLOps Engineer

Mode: Hybrid

Experience : 4 to 7 Years

Location : Hyderabad (Priority)/Bengaluru locations only

Notice Period : Immediate Joiner

Job Summary:

We are looking for a skilled and proactive ML Engineer with strong expertise in Python, Databricks, and Machine Learning model development. The ideal candidate should be proficient in building scalable data pipelines and deploying ML models, with a working knowledge of MLOps principles and tooling. This role offers an opportunity to work on impactful AI/ML initiatives in a collaborative environment.

Key Responsibilities:

• Develop and maintain machine learning pipelines for training, testing, and deploying models

• Design and implement infrastructure for managing and monitoring machine learning models

• Work with data scientists to build scalable, efficient, and automated model training and testing processes

• Collaborate with software engineers to integrate machine learning models into production systems

• Automate and optimize the deployment and scaling of machine learning models in a distributed computing environment

• Monitor and troubleshoot machine learning systems and infrastructure to ensure high availability and performance

• Develop and maintain documentation and best practices for MLOps processes and procedures.

Experience:

Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field

• 3+ years of experience in MLOps or related field, including building and deploying machine learning models at scale

•Proficiency in programming languages such as Python, Java, and C++

•Experience with machine learning frameworks such as TensorFlow, PyTorch, and Keras

• Experience with containerization technologies such as Docker and Kubernetes

• Strong understanding of DevOps principles and practices

• Experience with cloud computing platforms such as AWS, Azure, or Google Cloud

Job Description

Qualified applicants with experience in the following role or comparable job titles are encouraged to apply:

• Audio AI Engineer
• Speech AI Engineer
• Audio Software Engineer
• Speech Processing Engineer
• Audio Machine Learning Engineer
• Machine Learning Engineer – Speech
• AI Engineer – Audio
• Speech Processing Engineer
• Research Engineer – Speech AI
• Research Scientist – Speech
• Speech Recognition Engineer
• ASR Engineer
• Automatic Speech Recognition Engineer
• Voice AI Engineer
• Speech Scientist
• Audio Research Engineer
• Audio Systems Engineer
• Audio Software Engineer
• Audio R&D Engineer
• Speech Research Engineer
• Audio DSP Engineer
• Voice Processing Engineer
• Acoustic AI Engineer
• Research Engineer – Speech AI
• Research Scientist – Speech
• AI Engineer – Speech
• AI Engineer – Audio

Requirement:

a) Working on Design, Development, testing and deployment of different speech enhancement products using frameworks like PyTorch and TensorFlow.

b) Working on enhancing existing speech enhancement products w.r.t

performance, low latency and less computation.

c) Work on improvement of adapting the model to multi channels.

d) Should be self-motivated to learn and explore new areas and able to work independently and contribute

Experience

a) Good understanding of signal processing and machine learning.

b) Hands-on experience with Deep Learning techniques like CNNs, RNNs, LSTMs, Transformers, etc., for speech processing is essential.

c) Good understanding of: Linear algebra, Optimization techniques, Statistics and pattern recognition

d) Minimum 3 years of work experience in the Audio/Speech domain

e) Good programming skills in C/C++, Python, AI/ML frameworks.

Qualification

Bachelor's / Master’s Degree from AI/ML, CSE, ECE, or PhD

Job Title: Senior AI/ML Researcher – Large Language Models

Location: Bengaluru, India

Experience: Ph.D. or M.Tech (3–4 years research experience post-M.Tech)

Employment Type: Full-time

Company Overview

Big Air Lab (https://www.bigairlab.com/) operates at the edge of applied AI where foundational research meets real-world deployment. We craft intelligent systems that think in teams, adapt with context, and deliver actionable insights across domains.

Position Summary

We’re seeking a Senior AI/ML Researcher who lives and breathes large-scale models, algorithms, and cutting-edge machine learning. If you’re someone who wants to push boundaries in LLMs, predictive modeling, and applied AI research — while also guiding a team to turn theory into real-world solutions — this is your role.

You’re not just an academic — you’re a builder and a mentor. You’ll drive independent research, publish in reputed journals and conferences, and ensure that what’s on paper becomes code, experiments, and scalable AI solutions. You’ll lead from the front — coding, experimenting, mentoring, and showing what world-class research execution looks like.

Key Responsibilities

• Conduct independent research in Large Language Models (LLMs) and related predictive machine learning fields.

• Lead, mentor, and manage a research team comprising of senior members and interns.

• Publish research findings in reputed journals (preferred) or present at leading conferences.

• Develop and experiment with advanced AI and machine learning algorithms.

• Build, validate, and deploy models using PyTorch and other deep learning frameworks.

• Apply rigorous Object-Oriented Programming principles for developing scalable AI solutions.

• Collaborate closely with cross-functional teams to transition research into applicable solutions.

• Provide thought leadership and strategic guidance within the organization.

Required Qualifications

• Ph.D. in Computer Science, AI, or related fields (IIT or top-tier research institute preferred).
• Candidates with Ph.D. require no additional experience.
• M.Tech/MS in Computer Science, AI, or related fields from a top research institute with 3–4 years of dedicated research experience.
• Proven track record of research in LLMs or related AI domains, demonstrated by at least:
• 1 journal publication (preferred), or
• 4 high-quality conference papers.
• Strong expertise in PyTorch and other deep learning frameworks.
• Solid proficiency in Object-Oriented Programming (OOP).

Preferred Attributes

• Detail-oriented with a rigorous analytical mindset.

• Highly capable of conducting and guiding independent research.

• Strong written and verbal communication skills.

• Ability to clearly articulate complex research findings and strategic insights.

Why Join Big Air Lab?

• Lead groundbreaking research in cutting-edge AI technologies.

• Shape the direction of our newly formed R&D department.

• Enjoy significant professional growth, influence, and recognition within the AI research community.

About the Role

We're looking for a Senior AI/ML Engineer who thrives at the intersection of research and production — someone who doesn't just build models, but ships systems that scale. You'll own the full ML lifecycle: architecting robust data pipelines, training and rigorously evaluating models, and deploying them into high-throughput production environments. This is a role for engineers who want their work to move fast, break assumptions (not systems), and directly shape how AI gets built at scale. If you're energized by turning cutting-edge research into real-world impact, this is your next challenge.

Key Responsibilities

– Design, build, and maintain end-to-end ML pipelines — from raw data ingestion to model serving — with a focus on scalability and reliability.

– Develop, train, and rigorously evaluate ML/DL models using sound experimentation practices (A/B testing, offline/online metrics, statistical validation).

– Own the MLOps lifecycle: implement CI/CD pipelines for model training and deployment, version control for datasets and models, and automated retraining workflows.

– Deploy and monitor models in production using containerized, orchestrated infrastructure (Docker, Kubernetes), ensuring low-latency, high-availability inference.

– Collaborate cross-functionally with Data Engineering, Product, and Backend teams to translate business problems into scalable ML solutions.

– Optimize model performance for cost, latency, and accuracy trade-offs across cloud-based training and inference environments.

– Mentor and guide junior engineers and data scientists — conducting code reviews, sharing best practices, and raising the technical bar of the team.

– Stay current with emerging research (LLMs, generative AI, RAG architectures) and proactively identify opportunities to apply them to existing products.

Required Technical Skills

Languages

– Expert-level Python; working knowledge of R or C++ is a plus.

Frameworks

– TensorFlow, PyTorch, Scikit-Learn, Keras.

Data & Cloud

– Strong SQL and NoSQL fundamentals.

– Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure), specifically their ML tooling — SageMaker, Vertex AI, or equivalent.

– Experience with distributed data processing frameworks: Spark, Hadoop.

MLOps & Deployment

– Proficiency with Docker and Kubernetes for containerized deployment.

– Experience building CI/CD pipelines for ML workflows.

– Familiarity with experiment tracking and pipeline orchestration tools like MLflow or Kubeflow.

Advanced / Nice-to-Have

– Practical experience with LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG) architectures.

– Experience fine-tuning transformer-based models for domain-specific use cases.

Qualifications & Experience

– Bachelor's, Master's, or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.

– 5+ years of hands-on experience building, training, and deploying production-grade AI/ML models at scale.

– Demonstrated track record of taking models from prototype to production in a real-world business setting.

What We Offer

– Competitive compensation, benchmarked to top-tier tech talent in the industry.

– Comprehensive health insurance coverage for you and your family.

– Annual learning & development stipend for courses, certifications, and conferences.

– A collaborative, high-ownership culture where engineering excellence is celebrated.

– The opportunity to work on cutting-edge AI systems with direct, visible impact.

About Tech Transient

Tech Transient is an AI consulting and digital transformation firm headquartered in Coimbatore, Tamil Nadu.

We partner with enterprises and growth-stage companies to design and deliver intelligent digital products — spanning mobile applications, cloud platforms, and AI-driven solutions. Our mission is to translate emerging technology into measurable business outcomes.

Job Title: Associate AI/ML Engineer

Location: Gurugram, Haryana

Employment Type: Full-Time

About Aaizel Tech

Aaizel Tech is a pioneering tech startup at the intersection of cybersecurity, AI, geospatial solutions, and more. We drive innovation by delivering transformative technology solutions across industries. As a growing startup, we are looking for passionate and versatile professionals eager to work on cutting-edge projects in a dynamic environment.

Role Overview

As a Associate AI/ML Engineer at Aaizel Tech, you will lead the design, development, and deployment of advanced Machine Learning models and AI solutions. You will work on projects ranging from predictive analytics and NLP to computer vision and anomaly detection. You will also mentor a team of AI/ML professionals, collaborate with cross-functional teams, and drive innovation by integrating state-of-the-art research with scalable production systems.

Key Responsibilities

1. Model Development & Optimization

Design & Implementation:

• Architect and develop end-to-end ML solutions for applications such as predictive analytics, anomaly detection, computer vision, and NLP.
• Utilize advanced techniques including deep learning (CNNs, RNNs), reinforcement learning, and generative models (GANs) to address complex challenges.

Optimization:

• Fine-tune model parameters using techniques such as hyperparameter tuning (Grid Search, Bayesian Optimization, Neural Architecture Search).
• Optimize models for both accuracy and inference speed to meet real-time processing requirements.

2. Advanced Data Engineering & Integration

Data Pipeline Development:

• Build robust ETL pipelines using libraries like Pandas, NumPy, and PySpark to process large-scale datasets from satellite imagery, IoT sensors, and real-time streams.
• Integrate data from diverse sources (APIs, databases, big data platforms like Hadoop and Apache Kafka) to support real-time analytics.

Data Quality & Preprocessing:

• Implement data cleansing, feature engineering, and transformation pipelines to ensure high-quality inputs for ML models.

3. Research & Innovation

Algorithm Research:

• Conduct research on state-of-the-art ML techniques including Transfer Learning, Transformer models, and AutoML to enhance model performance.
• Innovate new algorithms for specialized tasks such as geospatial analysis, environmental modeling, or cybersecurity threat detection.

Prototyping & Experimentation:

• Develop proof-of-concept models and prototypes to validate new approaches before production deployment.

4. Deployment, MLOps & Performance Monitoring

Model Deployment:

• Deploy models using containerization (Docker) and orchestration tools (Kubernetes) to ensure scalable and efficient production environments.
• Work with cloud platforms (AWS, Azure, GCP) and model serving solutions (TensorFlow Serving, ONNX, TorchServe) for high-throughput inference.

MLOps & Lifecycle Management:

• Implement CI/CD pipelines for ML models, ensuring seamless updates and versioning.
• Develop monitoring dashboards (using Prometheus, Grafana) to track model performance and trigger retraining based on real-time feedback.

5. Collaboration & Leadership

Cross-Functional Teamwork:

• Collaborate closely with data engineers, software developers, domain experts, and product managers to integrate AI solutions into end-to-end products.

Mentorship & Code Quality:

• Provide technical leadership and mentorship to junior AI/ML engineers, ensuring adherence to coding standards and best practices.
• Participate in code reviews, maintain detailed documentation, and foster a culture of continuous learning.

Recommended Technology Stack

Backend Framework:

• Python (Django/FastAPI): Ideal for API integration, leveraging Python’s rich AI/ML ecosystem.

AI/ML Frameworks:

• PyTorch + Hugging Face Transformers + scikit-learn: For flexibility in research, multilingual NLP tasks, and classical ML pipelines.

Data Engineering:

• Apache Kafka + Apache Spark + Apache NiFi: To handle both real-time data streaming and batch processing.

Database & Storage:

• PostgreSQL with TimescaleDB extension: For structured and time-series data storage.

DevOps & Monitoring:

• Docker, Kubernetes, GitLab CI/CD, Prometheus/Grafana: For containerized deployments, continuous integration, and comprehensive monitoring.

Media Processing:

• OpenCV, FFmpeg, Tesseract OCR, Wav2Vec2: To support image, video, and speech-to-text processing where needed.

Required Skills & Qualifications

Technical Expertise:

• Experience:
• 5+ years in Machine Learning, AI research, or a related field with a proven track record of delivering production-level AI solutions.
• Programming & Frameworks:
• Expertise in Python and hands-on experience with frameworks like PyTorch, TensorFlow, and scikit-learn.
• Experience with Hugging Face Transformers for NLP applications.
• Data Engineering:
• Proficiency in building data pipelines using Pandas, NumPy, PySpark, and integrating data from diverse sources.
• Familiarity with big data platforms and real-time data processing frameworks.
• Model Deployment & MLOps:
• Hands-on experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for ML models.
• Experience with cloud deployment and model serving solutions.
• Research & Innovation:
• Demonstrated ability to apply advanced ML techniques (deep learning, transfer learning, reinforcement learning) to solve real-world problems.
• Testing & Optimization:
• Strong background in model evaluation, hyperparameter tuning, and performance optimization.

Soft Skills:

• Exceptional problem-solving and analytical abilities.
• Strong communication skills, with the ability to present complex technical concepts to diverse stakeholders.
• Leadership and mentoring experience, with a collaborative approach to working in cross-functional teams.
• Ability to thrive in a fast-paced, dynamic environment and drive continuous innovation.

Educational Background:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field from a reputed institution.

What We Offer

• Innovative Projects: Engage in cutting-edge AI/ML projects that influence product strategy and technological innovation.
• Professional Growth: Opportunities for continuous learning, mentorship, and career advancement.
• Collaborative Culture: Work within a diverse team of experts passionate about pushing the boundaries of technology.
• Impactful Work: Play a key role in shaping AI-driven solutions and driving real-world impact.

About Us

We are a fast-growing startup based in Pune, India, specializing in cutting-edge Data Science and Data Engineering solutions. Our team of dedicated professionals is committed to solving complex data challenges for companies worldwide.

Our Culture We foster a vibrant startup culture that values: • Intellectual curiosity • Continuous learning • Positive work environment • Collaborative problem-solving

Role Overview

We are seeking a versatile and proactive Data Scientist to join our dynamic team. The ideal candidate will possess a blend of technical expertise in modern AI/ML technologies, strategic planning, and effective communication skills. This role demands critical thinking, applying data science and problem-solving skills to a wide variety of real-world problems, adaptability to rapidly evolving technologies, and a strong foundation in both traditional and generative AI principles.

Key Responsibilities • Deliver end-to-end data science projects by applying Machine Learning and Deep Learning fundamentals to solve complex problems • Derive actionable insights for a variety of problems, industries, and domains using statistical analysis and advanced data science techniques • Develop high-quality software solutions with Python and other programming languages. Collaborate with developers to understand and improve existing code or create new solutions • Build and deploy production-ready LLM applications using modern frameworks and best practices • Design and implement RAG (Retrieval-Augmented Generation) architectures using vector databases and embedding models • Perform prompt engineering and optimization to maximize LLM performance for specific use cases • Implement agentic AI systems and multi-agent workflows for complex automation tasks • Evaluate and benchmark LLM outputs using appropriate metrics and testing frameworks • Build sophisticated data pipelines for large-scale data processing using modern orchestration tools • Optimize database performance and create efficient SQL queries • Deploy and monitor ML models in production using MLOps practices and containerization • Practice active listening to understand project requirements and team inputs • Collaborate with clients to translate business requirements into data science solutions • Communicate complex ideas and results clearly to stakeholders through both verbal and written formats • Apply responsible AI principles and ensure ethical considerations in model development • Demonstrate punctuality and a strong sense of ownership in all tasks • Plan strategically and multitask efficiently to meet project deadlines • Employ critical thinking to break down problems and debug effectively • Take initiative and be biased towards action to drive project progress Required Skills Core Programming & ML • Strong Python programming skills with hands-on project experience • Expertise in Machine Learning and Deep Learning algorithms (Random Forests, GBMs, Neural Networks, CNNs, RNNs, Transformers, Ensemble methods) • Proficiency in TensorFlow or PyTorch, along with scikit-learn and pandas • Familiarity with modern ML techniques: Transfer Learning, Few-shot Learning, Self-supervised Learning • Experience with NLP, Computer Vision, or Time Series Analysis Generative AI & LLMs • Hands-on experience with LLM providers (OpenAI, Anthropic Claude, Google Gemini, or open-source models) • Proficiency with GenAI orchestration frameworks (LangChain, LangGraph, LlamaIndex, or DSPy) • Experience building RAG applications with vector databases (Pinecone, Weaviate, Chroma, FAISS) • Strong prompt engineering skills and understanding of prompt optimization techniques • Knowledge of fine-tuning techniques (LoRA, QLoRA) and when to apply them • Understanding of LLM evaluation metrics and benchmarking methodologies • Familiarity with agentic AI architectures and multi-agent systems MLOps & Deployment • Experience with MLOps practices and tools (MLflow, Kubeflow, Weights & Biases) • Proficiency with containerization using Docker and orchestration with Kubernetes • Experience with cloud platforms (AWS, Azure, or GCP) for ML model deployment and monitoring • Understanding of CI/CD pipelines for ML applications • Knowledge of model serving frameworks and API development (FastAPI, Flask, or Django) Data Engineering & Databases • Solid understanding of SQL, including advanced concepts like windowing functions and query optimization • Experience with data pipeline orchestration tools (Airflow, Prefect, or similar) • Familiarity with both SQL and NoSQL databases Soft Skills & Professional Attributes • Strong critical thinking and problem-solving skills • Excellent written and verbal communication abilities • Demonstrated ability to work well in a team and independently • High degree of flexibility and adaptability to rapidly evolving technologies • Understanding of AI safety principles and responsible AI practices

Nice-to-Have • Experience with big data technologies (Spark, Hadoop, Databricks) • Familiarity with BI tools and dashboard creation (Tableau, Power BI, Looker) • Knowledge of graph databases and knowledge graph construction • Experience with real-time streaming data processing • Active participation in data science competitions (Kaggle, DrivenData) • Contributions to open-source AI/ML projects or technical blog • Experience with multimodal AI models (vision-language models, audio processing) • Published research papers or conference presentations

Qualifications • Data Scientist I: 0-2 years of hands-on experience in Data Science projects • Data Scientist II: 2-5 years of hands-on experience in Data Science projects • Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field • Demonstrated commitment to continuous learning through courses, certifications, or self-study (especially in GenAI and modern ML techniques)

What We Offer • Competitive salary commensurate with experience • Opportunity to work on diverse, cutting-edge AI/ML projects • Collaborative and innovation-driven work environment • Rapid growth and continuous learning opportunities • Exposure to latest AI technologies and industry best practices

Link for application -  https://forms.gle/9GENVfPeXdtgi7Zj7

About the Role

We are looking for passionate and driven interns across multiple technology domains including Frontend Development, Backend Development, DevOps, AI/ML, and Data Engineering. This internship offers hands-on experience in real-world projects, collaboration with cross-functional teams, and exposure to modern tools and technologies. 

Domains & Responsibilities 

Frontend Development 

• Build responsive and user-friendly web interfaces  
• Translate UI/UX designs into functional applications  
• Optimize performance and ensure cross-browser compatibility  

 Backend Development 

• Develop APIs and server-side logic  
• Work with databases and data storage solutions  
• Ensure application security and performance  

 DevOps 

• Assist in CI/CD pipeline setup and automation  
• Manage deployments and cloud infrastructure  
• Monitor system performance and reliability  

 AI / Machine Learning 

• Develop and train ML models  
• Work on NLP, automation, or AI-driven features  
• Analyze datasets and evaluate model performance  

 Data Engineering 

• Build and maintain data pipelines (ETL/ELT)  
• Ensure data quality and availability  
• Work with large datasets and optimize data workflows  

 Required Skills (Any Domain) 

• Frontend: HTML, CSS, JavaScript, React/Vue/Angular  
• Backend: Node.js / Python / Java / PHP, APIs, databases  
• DevOps: Linux, Git, CI/CD basics, cloud fundamentals  
• AI/ML: Python, ML basics, TensorFlow/PyTorch/Scikit-learn  
• Data Engineering: SQL, Python, data processing concepts  

 Good to Have 

• Knowledge of Git and version control  
• Basic understanding of cloud platforms (AWS/Azure/GCP)  
• Problem-solving mindset and willingness to learn  
• Exposure to real-world or academic projects  

 Who Should Apply 

• Students or recent graduates in Computer Science, IT, or related fields  
• Candidates with strong interest in any of the above domains  
• Self-learners with project experience are highly encouraged  

 Internship Details 

• Duration: 3–6 months  
• Mode: Remote  
• Certificate + PPO (Pre-Placement Offer) based on performance  

 What You’ll Gain 

• Hands-on experience with real projects  
• Mentorship from experienced professionals  
• Exposure to industry tools and workflows  
• Opportunity to convert to a full-time role 

Job Description: AI/ML Engineer

Location: Gurgaon, Haryana, India

Position: AI/ML Engineer -Intern

Stipend: As per industry standards

About Aaizel Tech

Aaizel Tech is a pioneering tech startup at the intersection of cybersecurity, AI, geospatial solutions, and more. We drive innovation by delivering transformative technology solutions across industries. As part of our dynamic team, you'll gain hands-on experience and learn from experts who are dedicated to pushing the boundaries of what's possible.

Key Responsibilities:

1. Model Development and Optimization:

• Design, implement, and deploy ML models for diverse applications, including predictive analytics, anomaly detection, and computer vision, tailored for cybersecurity, climate monitoring, and geospatial data analysis.
• Utilize deep learning (CNNs, RNNs), reinforcement learning, and generative models (GANs) for complex problem-solving.

1. Advanced Data Engineering:

• Develop ETL pipelines and preprocessing scripts using Pandas, Numpy, and PySpark to handle large datasets from satellite imagery, IoT sensors, and real-time data sources.
• Integrate data from APIs, databases, and big data platforms (Hadoop, Apache Kafka) to support real-time analytics.

1. Algorithm Research and Development:

• Research state-of-the-art ML techniques, including Transfer Learning, Transformer models, and AutoML, to enhance model performance.
• Innovate new algorithms for geospatial analysis, such as object detection on aerial imagery, spatial clustering, and environmental modeling.

1. Deployment and MLOps:

• Deploy models using Docker, Kubernetes, and CI/CD pipelines, ensuring robust model lifecycle management.
• Work with cloud platforms (AWS, Azure, GCP) to implement scalable model serving solutions, using technologies like Sage Maker, TensorFlow Serving, and ONNX.

1. Performance Evaluation and Tuning:

• Perform model evaluation using advanced metrics (F1 Score, AUC-ROC, Intersection over Union for spatial data) and optimize for both accuracy and inference speed.
• Implement hyperparameter tuning using Bayesian Optimization, Grid Search, and Neural Architecture Search.

6. Performance Evaluation and Tuning:

• Perform model evaluation using advanced metrics (F1 Score, AUC-ROC, Intersection over Union for spatial data) and optimise for both accuracy and inference speed.
• Implement hyperparameter tuning using Bayesian Optimization, Grid Search, and Neural Architecture Search.

1.      7. Collaboration and Code Quality:

• Collaborate with data engineers, cybersecurity experts, and geospatial analysts to integrate ML solutions into end-to-end products.
• Adhere to coding standards, participate in code reviews, and maintain high-quality codebases using Git, Jira, and Confluence.

     8.Monitoring and Maintenance:

• Develop monitoring dashboards using Grafana, Prometheus, or similar tools to track model performance post-deployment.
• Implement feedback loops for model retraining based on real-time data and evolving application needs.

Required Skills, Qualification and Experience:

Educational Background: Bachelor’s/Master’s in Computer Science, Data Science, Machine Learning, or a related field from preferably top-tier       institutions.

• Technical Proficiency:
• Strong skills in Python, PyTorch, TensorFlow, and Scikit-learn.
• Proficiency in SQL, NoSQL databases (MongoDB, Cassandra), and data warehousing solutions (Redshift, BigQuery).
• Problem-Solving: Analytical mindset with the ability to solve complex problems using data-driven approaches.
• Communication: Capable of translating technical details into actionable insights for diverse stakeholders.
• Adaptability: Ability to thrive in a high-paced, dynamic startup environment with a focus on rapid iteration and delivery.

Job Title: Product Lead or Tech Lead (AI & Infrastructure)

Location- Delhi

Job type: Full time, On site

About Us: TIMBLE is leading Authentication Company, delivering cutting edge technology and alternate data analysis for Identity management, Onboarding & Verification and Business Intelligence. We provide solutions across three verticals

1. BFSI Solutions

2. KYC and background check Solutions

3. AI Solutions

Role Overview-You will be the architectural backbone of Timble’s AI engine. This role requires a strong backend & systems mindset with exposure to AI/ML systems—balancing the development of high-accuracy fraud detection models with the scalable infrastructure required to run them.

Key Responsibilities

·      Engineering Leadership: Lead the development of our core AI products, including Bank Statement Analyzers, Face Match technology, and Electronic Residence Physical Verification (ERPV).

·      AI/ML Architecture: Design and deploy AI/ML-driven systems for document intelligence, fraud detection, and automation to enhance real-time intelligence.

·      Delivery Ownership: Take end-to-end ownership of features and ensure timely delivery in high-stakes production environments.

·      System Design & Scalability: Design and optimize high-throughput, low-latency API systems capable of handling real-world production loads across our 30+ high-quality APIs.

·      Hands-on Contribution: Remain hands-on with code when required, especially for critical modules, core architecture decisions, and troubleshooting.

·      Practical AI Application: Work on integrating and scaling AI/ML components in production. You must have the ability to apply complex AI solutions to solve real-world business problems.

·      Technical Strategy & InfoSec: Oversee Information Security protocols to protect proprietary financial data. Lead IP-related technical work, including patent-pending research for our authentication engines.

·      Mentorship: Act as the technical North Star for SDE-1 and SDE-2 engineers, instilling a culture of clean code, scalability, and cloud economics.

What We’re Looking For

·      Technical Expertise: Strong backend engineering expertise (Python or similar), with experience in building and maintaining scalable systems. Exposure to ML frameworks (TensorFlow/PyTorch) is a plus.

·      Domain Knowledge: Previous experience in Fintech, Cybersecurity, or BFSI tech stacks is highly preferred.

·      Infrastructure Skills: Solid experience with cloud infrastructure (AWS/GCP/Azure) and maintaining high availability.

·      Vision: The ability to translate complex fraud patterns into automated, executable code and a passion for "efficiency by design."

Learn more about us at: https://timbleglance.com

Role & Responsibilities

This role is a highly specialized business-facing AI team. We deliver professional AI products and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations. We look for individuals with strong, unique specializations to improve the overall strength of the team. This role is the right fit for you if you love working with customers, teammates, and fuelling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly.

Objectives of role:

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from Mosaic AI research to solve business problems
• Own production rollouts of consumer and internally facing GenAI applications
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap
• Required /Proven experience with the following:
• Experience in building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine- tuning, etc., with tools such as HuggingFace, LangChain, and DSPy
• Expertise in deploying production-grade GenAI applications, including evaluation and optimizations
• Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.
• Experience in building production-grade machine learning deployments on AWS, Azure, or GCP
• Graduate degree in a quantitative discipline like Computer Science or equivalent practical experience Passion for collaboration, life-long learning, and driving business value through AI
• [Preferred] Experience in using the Databricks Intelligence Platform and Apache Spark? to process large-scale distributed datasets
• We require fluency in English and willingness to work across time zones.

Ideal Candidate

• Strong AI/ML Engineer Profile
• Mandatory (Experience) : Must have 3+ years of experience in software engineering with atleast 1+ years in GenAI application development and production deployment
• Mandatory (GenAI Application Development): Must have proven experience building GenAI applications covering RAG pipelines, multi-agent systems, Text2SQL, and fine-tuning
• Mandatory (Production GenAI Deployment): Must have expertise deploying production-grade GenAI applications including model evaluation, optimisation, and ownership of full production rollouts
• Mandatory (ML & Data Science Tooling): Must have strong hands-on experience with core ML and data science tools including pandas, scikit-learn, and PyTorch
• Mandatory (Cloud ML Infrastructure): Must have experience building and deploying production-grade ML workloads on at least one of AWS, Azure, or GCP
• Mandatory (Communication): Must have strong English communication skills with the ability to work across time zones and collaborate cross-functionally with product, engineering, and business stakeholders
• Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month
• Mandatory (Note 2) : CTC is inclusive of 10% variable
• Mandatory (Note 3): Candidates should be available to join within May 31st or June first week max
• Preferred (Education): Must hold a graduate degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline or equivalent practical experience
• Preferred (Tools): Hands-on experience with the Databricks and Apache Spark
• Preferred (Pharma/Life Sciences Domain): Good understanding of Pharma Quality Standards and Practices or prior experience in pharmaceutical or life sciences contexts is a meaningful plus.

Strong AI/ML Engineer Profile

Mandatory (Experience) : Must have 3+ years of experience in software engineering with atleast 1+ years in GenAI application development and production deployment

Mandatory (GenAI Application Development): Must have proven experience building GenAI applications covering RAG pipelines, multi-agent systems, Text2SQL, and fine-tuning

Mandatory (Production GenAI Deployment): Must have expertise deploying production-grade GenAI applications including model evaluation, optimisation, and ownership of full production rollouts

Mandatory (ML & Data Science Tooling): Must have strong hands-on experience with core ML and data science tools including pandas, scikit-learn, and PyTorch

Mandatory (Cloud ML Infrastructure): Must have experience building and deploying production-grade ML workloads on at least one of AWS, Azure, or GCP

Mandatory (Communication): Must have strong English communication skills with the ability to work across time zones and collaborate cross-functionally with product, engineering, and business stakeholders

Example Responsibilities:

• Build and optimize model serving infrastructure with a focus on inference latency and cost optimization
• Architect efficient inference pipelines that balance latency, throughput, and cost across various acceleration options
• Develop monitoring and observability solutions for ML systems
• Collaborate with ML Engineers to establish best practices for optimized model deployment
• Implement cost-efficient, enterprise-scale solutions
• Collaborate in a cross-functional, distributed team for continuous system improvement
• Work with MLEs, QA Engineers, and DevOps Engineers
• Evaluate and implement new technologies and tools
• Contribute to architectural decisions for distributed ML systems

Experience and Qualifications:

• 5+ years of experience in software engineering with Python
• Experience with ML frameworks, particularly PyTorch
• Experience optimizing ML models with hardware acceleration (AWS Neuron , ONNX, TensorRT)
• Experience with AWS ML services and hardware-accelerated instances (Sagemaker, Inferentia,Trainium)
• Proven experience building and operating AWS serverless architectures
• Deep understanding of event-driven processing patterns, SQS/SNS and serverless caching solutions
• Experience with containerization using Docker and orchestration tools
• Strong knowledge of RESTful API design and implementation
• Proficiency in writing good quality & secure code and be familiar with static code analysis tools
• Excellent analytical, conceptual and communication skills in spoken and written English
• Experience applying Computer Science fundamentals in algorithm design, problem solving, and complexity analysis

Great to have Experience and Qualifications:

• Experience with any of the following: model compilation and quantization, performance profiling and benchmarking ML inference systems
• Experience working in regulated industries with strict compliance requirements for cloud-native solutions

About Swarmlens

At Swarmlens, we are building next-generation intelligent systems powered by Artificial Intelligence, Machine Learning, and Generative AI. Our mission is to create scalable, impactful, and production-ready AI solutions that solve real-world business challenges.

Role Overview

We are looking for a highly skilled Senior Generative AI Engineer with strong hands-on expertise in building and deploying Gen AI applications at scale. The ideal candidate should have deep experience working with LLMs, RAG pipelines, AI agents, prompt engineering, and production-grade AI systems.

You will work across the complete AI lifecycle — from model development and orchestration to deployment and optimization — while contributing to cutting-edge AI innovation.

Key Responsibilities

-Build and deploy end-to-end Generative AI solutions.

-Develop LLM-powered applications and AI copilots.

-Design and implement RAG pipelines and AI agent workflows.

-Work on prompt engineering, evaluation frameworks, and model optimization.

-Develop scalable AI architectures and production-ready ML systems.

-Collaborate with cross-functional teams to deliver AI-driven products.

-Optimize AI models for performance, scalability, and reliability.

-Build intelligent automation systems using modern Gen AI frameworks.

Required Skills & Qualifications

-4–6 years of experience in AI/ML or Generative AI roles.

-Strong hands-on expertise in Generative AI and Large Language Models. (LLMs).

-Experience with RAG systems, AI Agents, Prompt Engineering, and Model Evaluation.

-Proficiency in Python and AI/ML frameworks such as PyTorch, TensorFlow, LangChain, LlamaIndex, etc.

-Strong understanding of Machine Learning, Deep Learning, NLP, and data pipelines.

-Experience in deploying production-grade AI applications.

-Knowledge of vector databases, orchestration frameworks, and cloud environments is a plus.

-Strong analytical, problem-solving, and system design mindset.

Mandatory Requirement-

Candidates holding 4–6 years of relevant experience with a strong command of Generative AI are only preferred for this position. Please apply only if you have relevant hands-on experience in the same domain.

Why Join Us?

-Work on cutting-edge AI and Gen AI technologies.

-Opportunity to build impactful real-world AI products.

-Collaborative and innovation-driven work culture.

-Exposure to advanced AI research and scalable deployments.

-Career growth in a fast-growing AI startup environment.

Job Title: Product Lead or Tech Lead (AI & Infrastructure)

Location- Delhi

Job type: Full time, On site

About Us: TIMBLE is leading Authentication Company, delivering cutting edge technology and alternate data analysis for Identity management, Onboarding & Verification and Business Intelligence. We provide solutions across three verticals

1. BFSI Solutions

2. KYC and background check Solutions

3. AI Solutions

Role Overview-You will be the architectural backbone of Timble’s AI engine. This role requires a strong backend & systems mindset with exposure to AI/ML systems—balancing the development of high-accuracy fraud detection models with the scalable infrastructure required to run them.

Key Responsibilities

·      Engineering Leadership: Lead the development of our core AI products, including Bank Statement Analyzers, Face Match technology, and Electronic Residence Physical Verification (ERPV).

·      AI/ML Architecture: Design and deploy AI/ML-driven systems for document intelligence, fraud detection, and automation to enhance real-time intelligence.

·      Delivery Ownership: Take end-to-end ownership of features and ensure timely delivery in high-stakes production environments.

·      System Design & Scalability: Design and optimize high-throughput, low-latency API systems capable of handling real-world production loads across our 30+ high-quality APIs.

·      Hands-on Contribution: Remain hands-on with code when required, especially for critical modules, core architecture decisions, and troubleshooting.

·      Practical AI Application: Work on integrating and scaling AI/ML components in production. You must have the ability to apply complex AI solutions to solve real-world business problems.

·      Technical Strategy & InfoSec: Oversee Information Security protocols to protect proprietary financial data. Lead IP-related technical work, including patent-pending research for our authentication engines.

·      Mentorship: Act as the technical North Star for SDE-1 and SDE-2 engineers, instilling a culture of clean code, scalability, and cloud economics.

What We’re Looking For

·      Technical Expertise: Strong backend engineering expertise (Python or similar), with experience in building and maintaining scalable systems. Exposure to ML frameworks (TensorFlow/PyTorch) is a plus.

·      Domain Knowledge: Previous experience in Fintech, Cybersecurity, or BFSI tech stacks is highly preferred.

·      Infrastructure Skills: Solid experience with cloud infrastructure (AWS/GCP/Azure) and maintaining high availability.

·      Vision: The ability to translate complex fraud patterns into automated, executable code and a passion for "efficiency by design."

Learn more about us at: https://timbleglance.com

Budget: 35 LPA to 45 LPA

Work schedule is Mon to Fri, 3:30am to 12:30pm IST

Key Responsibilities:

• Design, develop, and deploy computer vision and machine learning models for analyzing visual and document-based data.
• Build pipelines that convert unstructured visual inputs into structured and usable information.
• Develop and evaluate models for tasks such as object detection, segmentation, document parsing, and image understanding.
• Apply OCR and related techniques to extract meaningful information from complex documents and imagery.
• Work with large datasets and build efficient training and evaluation pipelines.
• Handle real-world visual datasets that may contain noise, inconsistencies, incomplete information, or varying formats.
• Experiment with different approaches to solve challenging computer vision problems and evaluate tradeoffs between accuracy, performance, and complexity.
• Collaborate with product and engineering teams to integrate machine learning models into scalable production systems.
• Continuously improve model performance, accuracy, and robustness in real-world environments.
• Stay up to date with the latest developments in AI and computer vision and apply relevant techniques where appropriate.
• Actively leverage modern AI tools and frameworks to accelerate experimentation, development, and engineering workflows.

Requirements:

• 5+ years of hands-on experience building and deploying machine learning models, particularly in Computer Vision or document understanding.
• Strong proficiency in Python for machine learning and data processing.
• Hands-on experience with modern ML frameworks such as PyTorch and libraries in the Hugging Face ecosystem.
• Experience with computer vision tooling such as OpenCV.
• Experience with common ML and data science libraries such as scikit-learn, NumPy, and Pandas.
• Experience developing models for tasks such as segmentation, object detection, or document analysis.
• Experience working with large image datasets and building training pipelines.
• Solid understanding of model evaluation, data preprocessing, and performance optimization.
• Strong problem-solving skills and ability to work in a fast-paced product environment.
• Ability to collaborate effectively with cross-functional engineering and product teams.
• The candidate should be based in India
• Willing to work remotely full-time
• Work schedule is Mon to Fri, 3:30am to 12:30pm IST

Preferred Qualifications:

• Experience with TensorFlow or other deep learning frameworks.
• Experience working with OCR pipelines or document analysis systems.
• Experience deploying machine learning models in production environments.
• Experience with containerized deployments such as Docker or Kubernetes.
• Experience working with complex technical documents, diagrams, or structured visual data.
• Familiarity with spatial or geometry-related data problems.
• Experience with libraries such as Detectron2, MMDetection, or similar.
• Familiarity with frameworks used to integrate modern AI models into applications (e.g., LangChain or similar tooling).
• Contributions to open-source ML or computer vision projects are a plus.

Additional Information:

• The problems we work on involve complex visual and document-based data, so we value engineers who enjoy tackling challenging technical problems and experimenting with different approaches to reach practical solutions.
• Candidates are required to include links to relevant projects, GitHub repositories, research work, or examples of machine learning systems they have built.

Benefits:

• Flexible remote work opportunities with career development opportunities
• Engagement with a supportive and collaborative global team
• Competitive market based salary

JOB DETAILS:

- Job Title: Lead I - Data Science - Python, Machine Learning, Spark 

- Industry: Global Digital Transformation Solutions Provider

- Experience: 5-10 years

- Job Location: Pune

- CTC Range: Best in Industry

JD for Data Scientist

Hands-on experience with data analysis tools:

Proficient in using tools such as Python and R for data manipulation, querying, and analysis.

Skilled in utilizing libraries like Pandas, NumPy, and Scikit-Learn to perform in-depth data analysis and modeling.

Skilled in machine learning and predictive analytics:

Expertise in building, training, and deploying machine learning models using frameworks such as TensorFlow and PyTorch.

Capable of performing tasks like regression, classification, clustering, and recommendation, leading to data-driven predictions and insights.

Expertise in big data technologies:

Proficient in handling large datasets using big data tools such as Spark.

Skilled in employing distributed computing and parallel processing techniques to ensure efficient data processing, storage, and analysis, enabling enterprise-level solutions and informed decision-making

Skills: Python, SQL, Machine Learning, and Deep Learning, with mandatory expertise in Generative AI.

Must-Haves

5–9 years of relevant experience in Python, SQL, Machine Learning, and Deep Learning, with mandatory expertise in Generative AI

******

NP - Immediate joiners only

Location-Pune 

Introduction

About Us:

Mercari is a Japan-based C2C marketplace company founded in 2013 with the mission to “Create value in a global marketplace where anyone can buy & sell.” From being the first tech unicorn from Japan before its IPO in 2018 we have come a long way towards becoming a global player and continuously and diligently work towards our transformation journey with a strong focus on our mission.

Since its inception, Mercari Group has worked to grow its services, investing in both our people and technology. Over time Mercari has expanded from being the top player in the C2C marketplace in Japan to new geographies like the U.S. We have also successfully launched new businesses such as Merpay, which is a mobile payment service platform with a vision to create a society where anyone can realize their dreams through a new ecosystem centered not only on payment service but also on credit. Today, Mercari Group is made up of multiple subsidiary businesses including logistics, B2C platform, blockchain, and sports team management.

For our services to be utilized by people worldwide; however, there is still a mountain of work ahead of us. This endeavor naturally requires the capability of the best talent and minds, and that is exactly the reason for us to launch the India Center of Excellence. With your help, we will continue to take on the world stage and strive to grow into a successful global tech company.

Our Culture:

To achieve our mission at Mercari, our organization and each of our employees share the same values and perspectives. Our individual guidelines for action are defined by our four values: Go Bold, All for One, Be a Pro and Move Fast. Our organization is also shaped by our four foundations: Sustainability, Diversity & Inclusion, Trust & Openness, and Well-being for Performance. Regardless of how big Mercari gets, the culture will remain essential to achieving our mission and something we want to preserve throughout our organization. We invite you to read the Mercari Culture Doc which summarizes the behaviors and mindset shared by Mercari and its employees. We continue to build an environment where all of our members of diverse backgrounds are accepted and recognized, and where they can thrive while holding dear to Mercari’s culture.

Work Responsibilities

• Machine learning engineers working in the Recommendation domain develop the functions and services of the marketplace app Mercari through the development and maintenance of machine learning systems like Recommender systems while leveraging necessary infrastructure and companywide platform tools. 
• Mercari is actively applying advanced machine learning technology to provide a more convenient, safer, and more enjoyable marketplace. Machine learning engineers use the cloud and Kubernetes to operate and improve machine learning systems.

Bold Challenges

• We are looking for people who are interested in our services, mission, and values, and want to work where engineers can go bold, use the latest technology, make autonomous decisions, and take on challenges at a rapid pace.
• Develop and optimize machine learning algorithms and models to enhance recommendation system to improve discovery experience of users
• Collaborate with cross-functional teams and product stakeholders to gather requirements, design solutions, and implement features that improve user engagement
• Conduct data analysis and experimentation with large-scale data sets to identify patterns, trends, and insights that drive the refinement of recommendation algorithms
• Utilize machine learning frameworks and libraries to deploy scalable and efficient recommendation solutions.
• Monitor system performance and conduct A/B testing to evaluate the effectiveness of features.
• Continuously research and stay updated on advancements in AI/machine learning techniques and recommend innovative approaches to enhance recommendation capabilities.

Minimum Requirements:

• Over 5-9 years of professional experience in end-to-end development of large-scale ML systems in production
• Strong experience demonstrating development and delivery of end-to-end machine learning solutions starting from experimentation to deploying models, including backend engineering and MLOps, in large scale production systems.
• Experience using common machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, NumPy, pandas)
• Deep understanding of machine learning and software engineering fundamentals
• Basic knowledge and skills related to monitoring system, logging, and common operations in production environment
• Communication skills to carry out projects in collaboration with multiple teams and stakeholders

Preferred skills:

• Experience developing Recommender systems utilizing large-scale data sets
• Basic knowledge of enterprise search systems and related stacks (e.g. ELK)
• Functional development and bug fixing skills necessary to improve system performance and reliability
• Experience with technology such as Docker and Kubernetes
• Experience with cloud platforms (AWS, GCP, Microsoft Azure, etc.)
• Microservice development and operation experience with Docker and Kubernetes
• Utilizing deep learning models/LLMs in production
• Experience in publications at top-tier peer-reviewed conferences or journals

Employment Status

Full-time

Office

Bangalore

Hybrid workstyle

• We believe in high performance and professionalism. We work from office for 2 days/week and work from home 3 days/week
• To build a strong & highly-engaged organization in India, we highly encourage everyone to work from our Bangalore office, especially during the initial office setup phase
• We will continue to review and update the policy to address future organizational needs

Work Hours

• Full flextime (no core time)

*Flexible to choose working hours other than team common meetings

Media

Owned Media

• Mercari Engineering Portal
• AI at Mercari portal
• Mercan - Introduces the people that make Mercari
• Mercari US Blog

Related Articles

• Development Platforms and Platformers: On Rising to the Global Standard Ken Wakasa, Mercari CTO | mercan
• “I'm Not a Talented Engineer” Insists the Member-Turned-Manager Revamping Our Internal CS Tool | mercan
• Personalize to globalize:How Mercari is reshaping their app, their company, and the world | mercan
• The Providers of the Safe and Secure Mercari Experience: The TnS Team, Introduced by Its Members! | mercan

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows 

🤖 Data Scientist – Frontier AI for Data Platforms & Distributed Systems (4–8 Years)

Experience: 4–8 Years

Location: Bengaluru (On-site / Hybrid)

Company: Publicly Listed, Global Product Platform

🧠 About the Mission

We are building a Top 1% AI-Native Engineering & Data Organization — from first principles.

This is not incremental improvement.

This is a full-stack transformation of a large-scale enterprise into an AI-native data platform company.

We are re-architecting:

• Legacy systems → AI-native architectures
• Static pipelines → autonomous, self-healing systems
• Data platforms → intelligent, learning systems
• Software workflows → agentic execution layers

This is the kind of shift you would expect from companies like Google or Microsoft —

Except here, you will build it from day zero and scale it globally.

🧠 The Opportunity: This role sits at the intersection of three high-impact domains:

1. Frontier AI Systems: Large Language Models (LLMs), Small Language Models (SLMs), and Agentic AI

2. Data Platforms: Warehouses, Lakehouses, Streaming Systems, Query Engines

3. Distributed Systems: High-throughput, low-latency, multi-region infrastructure

We are building systems where:

• Data platforms optimize themselves using ML/LLMs
• Pipelines are autonomous, self-healing, and adaptive
• Queries are generated, optimized, and executed intelligently
• Infrastructure learns from usage and evolves continuously

This is: AI as the control plane for data infrastructure

🧩 What You’ll Work On

You will design and build AI-native systems deeply embedded inside data infrastructure.

1. AI-Native Data Platforms

• Build LLM-powered interfaces:
• Natural language → SQL / pipelines / transformations
• Design semantic data layers:
• Embeddings, vector search, knowledge graphs
• Develop AI copilots:
• For data engineers, analysts, and platform users

2. Autonomous Data Pipelines

• Build self-healing ETL/ELT systems using AI agents
• Create pipelines that:
• Detect anomalies in real time
• Automatically debug failures
• Dynamically optimize transformations

3. Intelligent Query & Compute Optimization

• Apply ML/LLMs to:
• Query planning and execution
• Cost-based optimization using learned models
• Workload prediction and scheduling
• Build systems that:
• Learn from query patterns
• Continuously improve performance and cost efficiency

4. Distributed Data + AI Infrastructure

• Architect systems operating at:
• Billions of events per day
• Petabyte-scale data
• Work with:
• Distributed compute engines (Spark / Flink / Ray class systems)
• Streaming systems (Kafka-class infra)
• Vector databases and hybrid retrieval systems

5. Learning Systems & Feedback Loops

• Build closed-loop AI systems:
• Execution → feedback → model updates
• Develop:
• Continual learning pipelines
• Online learning systems for infra optimization
• Experimentation frameworks (A/B, bandits, eval pipelines)

6. LLM & Agentic Systems (Infra-Aware)

• Build agents that understand data systems
• Enable:
• Autonomous pipeline debugging
• Root cause analysis for infra failures
• Intelligent orchestration of data workflows

🧠 What We’re Looking For

Core Foundations

• Strong grounding in:
• Machine Learning, Deep Learning, NLP
• Statistics, optimization, probabilistic systems
• Distributed systems fundamentals
• Deep understanding of:
• Transformer architectures
• Modern LLM ecosystems

Hands-On Expertise

• Experience building:
• LLM / GenAI systems (RAG, fine-tuning, embeddings)
• Data platforms (warehouse, lake, lakehouse architectures)
• Distributed pipelines and compute systems
• Strong programming skills:
• Python (ML/AI stack)
• SQL (deep understanding — query planning, optimization mindset)

Systems Thinking (Critical)

You think in systems, not components.

• Built or worked on:
• Large-scale data pipelines
• High-throughput distributed systems
• Low-latency, high-concurrency architectures
• Understand:
• Query optimization and execution
• Data partitioning, indexing, caching
• Trade-offs in distributed systems

🔥 What Sets You Apart (Top 1%)

• Built AI-powered data platforms or infra systems in production
• Designed or contributed to:
• Query engines / optimizers
• Data observability / lineage systems
• AI-driven infra or AIOps platforms
• Experience with:
• Multi-modal AI (logs, metrics, traces, text)
• Agentic AI systems
• Autonomous infrastructure
• Worked on systems at scale comparable to:
• Google (BigQuery-like systems)
• Meta (real-time analytics infra)
• Snowflake / Databricks (lakehouse architectures)

🧬 Ideal Background (Not Mandatory)

We often see strong candidates from:

• Data infrastructure or platform engineering teams
• AI-first startups or research-driven environments
• High-scale product companies

Experience building:

• Internal platforms used by 1000s of engineers
• Systems serving millions of users / high throughput workloads
• Multi-region, distributed cloud systems

🧠 The Kind of Problems You’ll Solve

• Can LLMs replace traditional query optimizers?
• How do we build self-healing data pipelines at scale?
• Can data systems learn from every query and improve automatically?
• How do we embed reasoning and planning into infrastructure layers?
• What does a fully autonomous data platform look like?

Background: We Commonly See (But Not Limited To)

Our team often includes engineers from top-tier institutions and strong research or product backgrounds, including:

• Leading engineering schools in India and globally
• Engineers with experience in top product companies, AI startups, or research-driven environments
• That said, we care far more about demonstrated ability, depth, and impact than pedigree alone.

Job Details

- Job Title: Director of Engineering

- Industry: SAAS

- Function – Information Technology

- Experience Required: 9-14 years

- Working Days: 6 days

- Employment Type: Full Time

- Job Location: Bangalore

- CTC Range: Best in Industry

Preferred Skills: TypeScript, AWS, NodeJS, mongodb, React.js, WebGL, Three.js, AI/ML, Docker,nKubernetes

Criteria

Candidate must be having 9+ years of engineering experience, with 3u20134 years in technical leadership

Hands-on expertise with React/Next.js, Node.js/Python, and AWS.

Ability to design scalable architectures for high-performance systems.

Should have AI/ML deployment experience

Strong 3D graphics/WebGL/Three.js knowledge.

Candidates should be from SAAS/Software/IT Services based startups or scaleup companies only

Job Description

The Role:

Company is hiring a hands-on Director of Engineering who codes, architects systems, and builds teams. You’ll set the technical foundation, drive engineering excellence, and own the architecture of our AI, 3D, and XR platform.

This is not a pure management role - expect to spend 50–60% of your time writing code, solving deep technical problems, and owning mission-critical systems. As we scale, this role transitions into CTO, taking full ownership of technical vision and long-term strategy. 

What You’ll Own:

1. Technical Leadership & Architecture

● Architect company’s full-stack platform across frontend, backend, infrastructure, and AI.

● Scale core systems: VersaAI engine, rendering pipeline, AR deployment, analytics.

● Make decisions on stack, scalability patterns, architecture, and technical debt.

● Own design for high-performance 3D asset processing, real-time rendering, and ML deployment.

● Lead architectural discussions, design reviews, and set engineering standards.

2. Hands-On Development

● Write production-grade code across frontend, backend, APIs, and cloud infra.

● Build critical features and core system components independently.

● Debug complex systems and optimize performance end-to-end.

● Implement and optimize AI/ML pipelines for 3D generation, CV, and recognition.

● Build scalable backend services for large-scale asset processing and real-time pipelines.

● Develop WebGL/Three.js rendering and AR workflows.

3. Team Building & Engineering Management

● Hire and grow a team of 5–8 engineers initially (scaling to 15–20).

● Establish engineering culture, values, and best practices.

● Build career frameworks, performance systems, and growth plans.

● Conduct 1:1s, mentor engineers, and drive continuous improvement.

● Set up processes for agile execution, deployments, and incident response.

4. Product & Cross-Functional Collaboration

● Work with the founder and product team on roadmap, feasibility, and prioritization.

● Translate product requirements into technical execution plans.

● Collaborate with design for UX quality and technical alignment.

● Support sales and customer success with integrations and technical discussions.

● Contribute technical inputs to product strategy and customer-facing initiatives.

5. Engineering Operations & Infrastructure

● Own CI/CD, testing frameworks, deployments, and automation.

● Create monitoring, logging, and alerting setups for reliability.

● Manage AWS infrastructure with a focus on cost and performance.

● Build internal tools, documentation, and developer workflows.

● Ensure enterprise-grade security, compliance, and reliability.

Tech Stack:

1. Frontend

React.js, Next.js, TypeScript, WebGL, Three.js

2. Backend

Node.js, Python, Express/FastAPI, REST, GraphQL

3. AI/ML

PyTorch, TensorFlow, CV models, Stable Diffusion, LLMs, ML pipelines

4. 3D & Graphics

Three.js, WebGL, Babylon.js, glTF, USDZ, rendering optimization

5. Databases

PostgreSQL, MongoDB, Redis, vector databases

6. Cloud & Infra

AWS (EC2, S3, Lambda, SageMaker), Docker, Kubernetes CI/CD: GitHub Actions

Monitoring: Datadog, Sentry

What We’re Looking For:

1. Must-Haves

● 9+ years of engineering experience, with 3–4 years in technical leadership.

● Deep full-stack experience with strong system design fundamentals.

● Proven success building products from 0→1 in fast-paced environments.

● Hands-on expertise with React/Next.js, Node.js/Python, and AWS.

● Ability to design scalable architectures for high-performance systems.

● Strong people leadership with experience hiring and mentoring teams.

● Ready to code, review, design, and lead from the front.

● Startup mindset: fast execution, problem-solving, ownership.

2. Highly Desirable

● AI/ML deployment experience (CV, generative AI, 3D reconstruction).

● Strong 3D graphics/WebGL/Three.js knowledge.

● Experience with real-time systems, rendering optimizations, or large-scale pipelines.

● Background in B2B SaaS, XR, gaming, or immersive tech.

● Experience scaling engineering teams from 5 → 20+.

● Open-source contributions or technical content creation.

● Experience working closely with founders or executive leadership.

Why Company:

● Hard, meaningful engineering problems at the intersection of AI, 3D, XR, and web tech.

● Build from day zero – architecture, team, and culture.

● Path to CTO as the company scales.

● High autonomy to drive technical decisions.

● Direct founder collaboration on product vision.

● High ownership, high-growth environment.

● Backed by global leaders: Microsoft, Google, NVIDIA, AWS.

Location & Work Culture:

● Location: HSR Layout, Bengaluru

● Schedule: 6 days a week, (5 days-in-office, Saturdays WFH)

● Culture: High-intensity, high-integrity, engineering-first

● Team: Young, ambitious, technically strong

Job Description

About the Role

We are seeking an experienced and hands-on Lead AI Engineer with 8-9 years of experience in developing, fine-tuning, and deploying machine learning and deep learning models, including Generative AI systems. The ideal candidate will have strong expertise in classification, anomaly detection, and time-series modeling, along with deep experience in Transformer-based architectures and modern LLM ecosystems.

This role requires technical leadership, architectural decision-making, and mentoring of AI engineers, while actively contributing to building scalable AI solutions. Expertise in model optimization, quantization, and Retrieval-Augmented Generation (RAG) pipelines is highly desirable.

Responsibilities

• Lead the design, development, and deployment of ML and deep learning models for classification, anomaly detection, forecasting, and natural language understanding tasks.
• Architect and build scalable AI and Generative AI solutions, including RAG pipelines for document search, Q&A, summarization, and enterprise knowledge systems.
• Design, train, and fine-tune deep learning models including RNNs, GRUs, LSTMs, and Transformer architectures (e.g., BERT, T5, GPT).
• Drive the fine-tuning and adaptation of large language models (LLMs) using techniques such as Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA.
• Apply model optimization techniques such as quantization, pruning, and efficient inference strategies to improve latency and reduce compute and memory footprint in production systems.
• Define and implement evaluation frameworks, track model performance, monitor model drift, and drive continuous model improvement.
• Lead collaboration with data engineering, backend, platform, and DevOps teams to productionize AI solutions using scalable infrastructure and CI/CD pipelines.
• Provide technical mentorship and guidance to junior and mid-level AI engineers and contribute to best practices in ML engineering.
• Ensure clean, reproducible code, maintain experiment tracking, documentation, and version control of models and datasets.
• Stay up to date with the latest advancements in LLMs, Generative AI, and AI infrastructure, and help drive adoption of new technologies.

Required Skills & Qualifications

• 8-9 years of hands-on experience in machine learning, deep learning, or data science roles.
• Strong programming expertise in Python and ML/DL libraries such as scikit-learn, pandas, PyTorch, and TensorFlow.
• Deep understanding of machine learning algorithms, deep learning architectures, and sequence/NLP modeling techniques.
• Extensive experience with Transformer models and open-source LLM ecosystems (e.g., Hugging Face Transformers).
• Hands-on experience building Generative AI applications and RAG-based systems using frameworks such as LangChain or LlamaIndex.
• Experience with model optimization and quantization techniques (dynamic/static quantization, INT8, etc.) for efficient inference.
• Strong understanding of embeddings, vector databases, and retrieval systems (e.g., FAISS, Pinecone, Azure AI Search).
• Experience with model evaluation, monitoring, and performance optimization in production environments.
• Familiarity with containerization (Docker), experiment tracking (MLflow), and CI/CD pipelines.
• Proven ability to lead technical initiatives and mentor engineering teams.

Preferred Qualifications

• Experience fine-tuning LLMs using SFT, LoRA, or QLoRA on domain-specific datasets.
• Exposure to MLOps platforms such as SageMaker, Vertex AI, or Kubeflow.
• Experience with distributed data processing frameworks like Spark and workflow orchestration tools such as Airflow.
• Contributions to research papers, technical blogs, patents, or open-source projects in ML, NLP, or Generative AI.
• Experience designing enterprise-scale AI platforms or AI-powered products.

Job Responsibilities

• Help develop an analytics platform that integrates insights from diverse data sources.
• Build, deploy, and test machine learning and classification models
• Train and retrain systems when necessary
• Design experiments, train and track performance of machine learning and AI models that meet specific business requirements
• ML and data labelling: Identify ways to gather and build training data with automated data labelling techniques and the creation of highly accurate training datasets.
• Automatic extraction of causal knowledge from diverse information sources such as databases, news, social media, videos and images etc.
• Develop customized machine learning solutions including data querying and knowledge extraction.
• Develop and implement approaches for extracting patterns and correlations from both internal and external data sources using time series machine learning models.
• Work in an Agile, collaborative environment, partnering with other scientists, engineers, consultants and database administrators of all backgrounds and disciplines to bring analytical rigor and statistical methods to the challenges of predicting behaviors.
• Distil insights from complex data, communicating findings to technical and non-technical audiences.
• Develop, improve, or expand in-house computational pipelines, algorithms, models, and services used in crop product development.
• Constantly document and communicate results of research on data mining, analysis, and modelling approaches.
• 5+ years of experience working with NLP and ML technologies.
• Proven experience as a Machine Learning Engineer or similar role.
• Experience with NLP/ML frameworks and libraries       
• Proficient with Python scripting language
• Background in machine learning frameworks like TensorFlow, PyTorch, Scikit Learn, etc.
• Demonstrated experience developing and executing machine learning, deep learning, data mining and classification models. Conversant with the latest in NLP and NLU models including transformer architectures and in creating explainable AI
• Ability to communicate the advantages and disadvantages of choosing specific models to various stakeholders
• Proficient with relational databases and SQL. Ability to write efficient queries and optimize the storage and retrieval of data within the database. Experience with creating and working on APIs, Serverless architectures and Containers.
• Creative, innovative, and strategic thinking; willingness to be bold and take risks on new ideas.

͏

͏

͏

͏

Mandatory Skills: AI : Artificial Intelligence .

Experience: 8-10 Years .

At Dolat Capital, we blend cutting-edge technology with quantitative finance to drive high-performance trading across Equities, Futures, and Options. We're a fast-moving team of traders, engineers, and data scientists building ultra-low latency systems and intelligent trading strategies.

🎯 What You’ll Work On

1. Designing and deploying high-frequency, high-sharpe trading strategies

2. Building low-latency, high-throughput trading infrastructure (C++/Python/Linux).

3. Leveraging AI/ML to detect alpha and market patterns from large datasets

Real-time risk systems, simulation tools, and performance optimization

4. Collaborating across tech and trading teams to push innovation in live markets.

🧠 What We’re Looking For

1. Master’s (U.S.) in CS or Computational Finance (MANDATORY)

2. 1–2 years of experience in a quant/tech-heavy role

3. Strong in C++, Python, algorithms, Linux, TCP/UDP

4. Experience with AI/ML tools like TensorFlow, PyTorch, or Scikit-learn

5. Passion for high-performance systems and market innovation.

JOB DETAILS:

* Job Title: Principal Data Scientist

* Industry: Healthcare

* Salary: Best in Industry

* Experience: 6-10 years

* Location: Bengaluru

Preferred Skills: Generative AI, NLP & ASR, Transformer Models, Cloud Deployment, MLOps

Criteria:

1. Candidate must have 7+ years of experience in ML, Generative AI, NLP, ASR, and LLMs (preferably healthcare).
2. Candidate must have strong Python skills with hands-on experience in PyTorch/TensorFlow and transformer model fine-tuning.
3. Candidate must have experience deploying scalable AI solutions on AWS/Azure/GCP with MLOps, Docker, and Kubernetes.
4. Candidate must have hands-on experience with LangChain, OpenAI APIs, vector databases, and RAG architectures.
5. Candidate must have experience integrating AI with EHR/EMR systems, ensuring HIPAA/HL7/FHIR compliance, and leading AI initiatives.

Job Description

Principal Data Scientist

(Healthcare AI | ASR | LLM | NLP | Cloud | Agentic AI)

Job Details

• Designation: Principal Data Scientist (Healthcare AI, ASR, LLM, NLP, Cloud, Agentic AI)
• Location: Hebbal Ring Road, Bengaluru
• Work Mode: Work from Office
• Shift: Day Shift
• Reporting To: SVP
• Compensation: Best in the industry (for suitable candidates)

Educational Qualifications

• Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
• Technical certifications in AI/ML, NLP, or Cloud Computing are an added advantage

Experience Required

• 7+ years of experience solving real-world problems using:
• Natural Language Processing (NLP)
• Automatic Speech Recognition (ASR)
• Large Language Models (LLMs)
• Machine Learning (ML)
• Preferably within the healthcare domain
• Experience in Agentic AI, cloud deployments, and fine-tuning transformer-based models is highly desirable

Role Overview

This position is part of company, a healthcare division of Focus Group specializing in medical coding and scribing.

We are building a suite of AI-powered, state-of-the-art web and mobile solutions designed to:

• Reduce administrative burden in EMR data entry
• Improve provider satisfaction and productivity
• Enhance quality of care and patient outcomes

Our solutions combine cutting-edge AI technologies with live scribing services to streamline clinical workflows and strengthen clinical decision-making.

The Principal Data Scientist will lead the design, development, and deployment of cognitive AI solutions, including advanced speech and text analytics for healthcare applications. The role demands deep expertise in generative AI, classical ML, deep learning, cloud deployments, and agentic AI frameworks.

Key Responsibilities

AI Strategy & Solution Development

• Define and develop AI-driven solutions for speech recognition, text processing, and conversational AI
• Research and implement transformer-based models (Whisper, LLaMA, GPT, T5, BERT, etc.) for speech-to-text, medical summarization, and clinical documentation
• Develop and integrate Agentic AI frameworks enabling multi-agent collaboration
• Design scalable, reusable, and production-ready AI frameworks for speech and text analytics

Model Development & Optimization

• Fine-tune, train, and optimize large-scale NLP and ASR models
• Develop and optimize ML algorithms for speech, text, and structured healthcare data
• Conduct rigorous testing and validation to ensure high clinical accuracy and performance
• Continuously evaluate and enhance model efficiency and reliability

Cloud & MLOps Implementation

• Architect and deploy AI models on AWS, Azure, or GCP
• Deploy and manage models using containerization, Kubernetes, and serverless architectures
• Design and implement robust MLOps strategies for lifecycle management

Integration & Compliance

• Ensure compliance with healthcare standards such as HIPAA, HL7, and FHIR
• Integrate AI systems with EHR/EMR platforms
• Implement ethical AI practices, regulatory compliance, and bias mitigation techniques

Collaboration & Leadership

• Work closely with business analysts, healthcare professionals, software engineers, and ML engineers
• Implement LangChain, OpenAI APIs, vector databases (Pinecone, FAISS, Weaviate), and RAG architectures
• Mentor and lead junior data scientists and engineers
• Contribute to AI research, publications, patents, and long-term AI strategy

Required Skills & Competencies

• Expertise in Machine Learning, Deep Learning, and Generative AI
• Strong Python programming skills
• Hands-on experience with PyTorch and TensorFlow
• Experience fine-tuning transformer-based LLMs (GPT, BERT, T5, LLaMA, etc.)
• Familiarity with ASR models (Whisper, Canary, wav2vec, DeepSpeech)
• Experience with text embeddings and vector databases
• Proficiency in cloud platforms (AWS, Azure, GCP)
• Experience with LangChain, OpenAI APIs, and RAG architectures
• Knowledge of agentic AI frameworks and reinforcement learning
• Familiarity with Docker, Kubernetes, and MLOps best practices
• Understanding of FHIR, HL7, HIPAA, and healthcare system integrations
• Strong communication, collaboration, and mentoring skills

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field. 

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field. 

What You’ll Do:

As a Sr. Data Scientist, you will work closely across DeepIntent Data Science teams located in New York, India, and Bosnia. The role will focus on building predictive models, implementing data-driven solutions to maximize ad effectiveness. You will also lead efforts in generating analyses and insights related to the measurement of campaign outcomes, Rx, patient journey, and supporting the evolution of the DeepIntent product suite. Activities in this position include developing and deploying models in production, reading campaign results, analyzing medical claims, clinical, demographic and clickstream data, performing analysis and creating actionable insights, summarizing, and presenting results and recommended actions to internal stakeholders and external clients, as needed.

• Explore ways to create better predictive models.
• Analyze medical claims, clinical, demographic and clickstream data to produce and present actionable insights.
• Explore ways of using inference, statistical, and machine learning techniques to improve the performance of existing algorithms and decision heuristics.
• Design and deploy new iterations of production-level code.
• Contribute posts to our upcoming technical blog.

Who You Are:

• Bachelor’s degree in a STEM field, such as Statistics, Mathematics, Engineering, Biostatistics, Econometrics, Economics, Finance, or Data Science.
• 5+ years of working experience as a Data Scientist or Researcher in digital marketing, consumer advertisement, telecom, or other areas requiring customer-level predictive analytics.
• Advanced proficiency in performing statistical analysis in Python, including relevant libraries, is required.
• Experience working with data processing, transformation and building model pipelines using tools such as Spark, Airflow, and Docker.
• You have an understanding of the ad-tech ecosystem, digital marketing and advertising data and campaigns or familiarity with the US healthcare patient and provider systems (e.g. medical claims, medications).
• You have varied and hands-on predictive machine learning experience (deep learning, boosting algorithms, inference…).
• You are interested in translating complex quantitative results into meaningful findings and interpretable deliverables, and communicating with less technical audiences orally and in writing.
• You can write production level code, work with Git repositories.
• Active Kaggle participant.
• Working experience with SQL.
• Familiar with medical and healthcare data (medical claims, Rx, preferred).
• Conversant with cloud technologies such as AWS or Google Cloud.

Role: Senior AI Engineer

Work Location: TechGenzi Coimbatore Office (ODC for Tiramai.ai)

Employment Type: Full-time

Experience: 2–5 years (Full-stack development with AI exposure)

About the Role & Work Location.

The selected candidate will be employed by Tiramai.ai and will work exclusively on Tiramai.ai projects. The role will be based out of TechGenzi’s Coimbatore office, which functions as an Offshore Development Center (ODC) supporting Tiramai.ai’s product and engineering initiatives.

Primary Focus

As an AI Engineer at our enterprise SaaS and AI-native organization, you will play a pivotal role in building secure, scalable, and intelligent digital solutions. This role combines full-stack development expertise with applied AI skills to create next-generation platforms that empower enterprises to modernize and act smarter with AI. You will work on AI-driven features, APIs, and cloud-native applications that are production-ready, compliance-conscious, and aligned with our mission of delivering responsible AI innovation.

Key Responsibilities

• Design, develop, and maintain full-stack applications using Python (backend) and React/Angular (frontend).
• Build and integrate AI-driven modules, leveraging GenAI, ML models, and AI-native tools into enterprise-grade SaaS products.
• Develop scalable REST APIs and microservices with security, compliance, and performance in mind.
• Collaborate with architects, product managers, and cross-functional teams to translate requirements into production-ready features.
• Ensure adherence to secure coding standards, data privacy regulations, and human-in-the-loop AI principles.
• Participate in code reviews, system design discussions, and continuous integration/continuous deployment (CI/CD) practices.
• Contribute to reusable libraries, frameworks, and best practices to accelerate AI platform development.

Skills Required

• Strong proficiency in Python for backend development.
• Frontend expertise in React.js or Angular with 2+ years of experience.
• Hands-on experience in full SDLC development (design, build, test, deploy, maintain).
• Familiarity with AI/ML frameworks (e.g., TensorFlow, PyTorch) or GenAI tools (LangChain, vector DBs, OpenAI APIs).
• Knowledge of cloud-native development (AWS/Azure/GCP), Docker, Kubernetes, and CI/CD pipelines.
• Strong understanding of REST APIs, microservices, and enterprise-grade security standards.
• Ability to work collaboratively in fast-paced, cross-functional teams with strong problem-solving and analytical skills.
• Exposure to responsible AI principles (explainability, bias mitigation, compliance) is a plus.

Growth Path

• AI Engineer (24 years) focus on full-stack + AI integration, delivering production-ready features.
• Senior AI Engineer (4–6 years) lead modules, mentor juniors, and drive AI feature development at scale.
• Lead AI Engineer (6–8 years) own solution architecture for AI features, ensure security/compliance, collaborate closely with product/tech leaders.
• AI Architect / Engineering Manager (8+ years) shape AI platform strategy, guide large-scale deployments, and influence product/technology roadmap.

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field. 

Position: Machine Learning Engineer 

Job Type: Full Time, Permanent 

Location : Baner, Pune 

We are looking for a visionary Machine Learning Engineer to bridge the gap between cutting-edge research and real-world applications. In this role, you won’t just be training models; you will be architecting the visual intelligence that powers our next generation of products. You will join a high-impact team dedicated to solving complex spatial and visual problems at scale. 

What You’ll Do 

• Architect & Innovate: Own the journey from research to production-ready computer vision pipelines. 
• Optimize Performance: Build high-speed deep learning models for real-time detection and recognition. 
• Lead with Data: Curate massive datasets and apply advanced engineering to maximize model accuracy. 
• Collaborate & Integrate: Partner with cross-functional teams to embed AI insights into core products. 
• Stay Ahead: Proactively prototype SOTA architectures to keep our solutions at the cutting edge. 
• 
  

What We’re Looking For 

• Expert in Python and OpenCV. You are framework-agnostic but a power user of PyTorch or TensorFlow. 
• 2–5 years of shipping ML products. You know how to move beyond research notebooks into scalable production code. 
• A problem solver obsessed with robust, explainable AI and clean, rigorous validation. 
• Strong mastery of the math behind CV—specifically geometry, linear algebra, and optimization. 

We are seeking a Developer Team Lead who will own the technical execution, architecture, and delivery of Super AI’s core platforms and customer implementations. This role requires a hands-on leader who can guide full-stack and AI engineers, ensure high-quality code, and translate business and government requirements into scalable, secure, and production-ready systems.

The ideal candidate is equally comfortable writing code, reviewing architectures, mentoring developers, and collaborating with product, pre-sales, and leadership teams.

Review Criteria:

• Strong MLOps profile
• 8+ years of DevOps experience and 4+ years in MLOps / ML pipeline automation and production deployments
• 4+ years hands-on experience in Apache Airflow / MWAA managing workflow orchestration in production
• 4+ years hands-on experience in Apache Spark (EMR / Glue / managed or self-hosted) for distributed computation
• Must have strong hands-on experience across key AWS services including EKS/ECS/Fargate, Lambda, Kinesis, Athena/Redshift, S3, and CloudWatch
• Must have hands-on Python for pipeline & automation development
• 4+ years of experience in AWS cloud, with recent companies
• (Company) - Product companies preferred; Exception for service company candidates with strong MLOps + AWS depth

Preferred:

• Hands-on in Docker deployments for ML workflows on EKS / ECS
• Experience with ML observability (data drift / model drift / performance monitoring / alerting) using CloudWatch / Grafana / Prometheus / OpenSearch.
• Experience with CI / CD / CT using GitHub Actions / Jenkins.
• Experience with JupyterHub/Notebooks, Linux, scripting, and metadata tracking for ML lifecycle.
• Understanding of ML frameworks (TensorFlow / PyTorch) for deployment scenarios.

Job Specific Criteria:

• CV Attachment is mandatory
• Please provide CTC Breakup (Fixed + Variable)?
• Are you okay for F2F round?
• Have candidate filled the google form?

Role & Responsibilities:

We are looking for a Senior MLOps Engineer with 8+ years of experience building and managing production-grade ML platforms and pipelines. The ideal candidate will have strong expertise across AWS, Airflow/MWAA, Apache Spark, Kubernetes (EKS), and automation of ML lifecycle workflows. You will work closely with data science, data engineering, and platform teams to operationalize and scale ML models in production.

Key Responsibilities:

• Design and manage cloud-native ML platforms supporting training, inference, and model lifecycle automation.
• Build ML/ETL pipelines using Apache Airflow / AWS MWAA and distributed data workflows using Apache Spark (EMR/Glue).
• Containerize and deploy ML workloads using Docker, EKS, ECS/Fargate, and Lambda.
• Develop CI/CT/CD pipelines integrating model validation, automated training, testing, and deployment.
• Implement ML observability: model drift, data drift, performance monitoring, and alerting using CloudWatch, Grafana, Prometheus.
• Ensure data governance, versioning, metadata tracking, reproducibility, and secure data pipelines.
• Collaborate with data scientists to productionize notebooks, experiments, and model deployments.

Ideal Candidate:

• 8+ years in MLOps/DevOps with strong ML pipeline experience.
• Strong hands-on experience with AWS:
• Compute/Orchestration: EKS, ECS, EC2, Lambda
• Data: EMR, Glue, S3, Redshift, RDS, Athena, Kinesis
• Workflow: MWAA/Airflow, Step Functions
• Monitoring: CloudWatch, OpenSearch, Grafana
• Strong Python skills and familiarity with ML frameworks (TensorFlow/PyTorch/Scikit-learn).
• Expertise with Docker, Kubernetes, Git, CI/CD tools (GitHub Actions/Jenkins).
• Strong Linux, scripting, and troubleshooting skills.
• Experience enabling reproducible ML environments using Jupyter Hub and containerized development workflows.

Education:

• Master’s degree in computer science, Machine Learning, Data Engineering, or related field. 

We are looking for enthusiastic engineers passionate about building and maintaining solutioning platform components on cloud and Kubernetes infrastructure. The ideal candidate will go beyond traditional SRE responsibilities by collaborating with stakeholders, understanding the applications hosted on the platform, and designing automation solutions that enhance platform efficiency, reliability, and value.

[Technology and Sub-technology]

• ML Engineering / Modelling

• Python Programming

• GPU frameworks: TensorFlow, Keras, Pytorch etc.

• Cloud Based ML development and Deployment AWS or Azure

[Qualifications]

• Bachelor’s Degree in Computer Science, Computer Engineering or equivalent technical degree

• Proficient programming knowledge in Python or Java and ability to read and explain open source codebase.

• Good foundation of Operating Systems, Networking and Security Principles

• Exposure to DevOps tools, with experience integrating platform components into Sagemaker/ECR and AWS Cloud environments.

• 4-6 years of relevant experience working on AI/ML projects

[Primary Skills]:

• Excellent analytical & problem solving skills.

• Exposure to Machine Learning and GenAI technologies.

• Understanding and hands-on experience with AI/ML Modeling, Libraries, frameworks, and Tools (TensorFlow, Keras, Pytorch etc.)

• Strong knowledge of Python, SQL/NoSQL

• Cloud Based ML development and Deployment AWS or Azure

Review Criteria

• Strong Senior Data Scientist (AI/ML/GenAI) Profile
• 5+ years of experience in designing, developing, and deploying Machine Learning / Deep Learning (ML/DL) systems in production
• Must have strong hands-on experience in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• 1+ years of experience in fine-tuning Large Language Models (LLMs) using techniques like LoRA/QLoRA, and building RAG (Retrieval-Augmented Generation) pipelines.
• Must have experience with MLOps and production-grade systems including Docker, Kubernetes, Spark, model registries, and CI/CD workflows

Preferred

• Prior experience in open-source GenAI contributions, applied LLM/GenAI research, or large-scale production AI systems
• Preferred (Education) – B.S./M.S./Ph.D. in Computer Science, Data Science, Machine Learning, or a related field.

Job Specific Criteria

• CV Attachment is mandatory
• Which is your preferred job location (Mumbai / Bengaluru / Hyderabad / Gurgaon)?
• Are you okay with 3 Days WFO?
• Virtual Interview requires video to be on, are you okay with it?

Role & Responsibilities

Company is hiring a Senior Data Scientist with strong expertise in AI, machine learning engineering (MLE), and generative AI. You will play a leading role in designing, deploying, and scaling production-grade ML systems — including large language model (LLM)-based pipelines, AI copilots, and agentic workflows. This role is ideal for someone who thrives on balancing cutting-edge research with production rigor and loves mentoring while building impact-first AI applications.

Responsibilities:

• Own the full ML lifecycle: model design, training, evaluation, deployment
• Design production-ready ML pipelines with CI/CD, testing, monitoring, and drift detection
• Fine-tune LLMs and implement retrieval-augmented generation (RAG) pipelines
• Build agentic workflows for reasoning, planning, and decision-making
• Develop both real-time and batch inference systems using Docker, Kubernetes, and Spark
• Leverage state-of-the-art architectures: transformers, diffusion models, RLHF, and multimodal pipelines
• Collaborate with product and engineering teams to integrate AI models into business applications
• Mentor junior team members and promote MLOps, scalable architecture, and responsible AI best practices

Ideal Candidate

• 5+ years of experience in designing, deploying, and scaling ML/DL systems in production
• Proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, or JAX
• Experience with LLM fine-tuning, LoRA/QLoRA, vector search (Weaviate/PGVector), and RAG pipelines
• Familiarity with agent-based development (e.g., ReAct agents, function-calling, orchestration)
• Solid understanding of MLOps: Docker, Kubernetes, Spark, model registries, and deployment workflows
• Strong software engineering background with experience in testing, version control, and APIs
• Proven ability to balance innovation with scalable deployment
• B.S./M.S./Ph.D. in Computer Science, Data Science, or a related field
• Bonus: Open-source contributions, GenAI research, or applied systems at scale