671 Machine Learning (ML) Jobs in India

About the Role

We are looking for a Junior React Native Developer with 1–2 years of hands-on experience in developing mobile applications using React Native for both Android and iOS platforms.

The ideal candidate should have strong fundamentals in React Native and JavaScript/TypeScript, along with exposure to AI/ML technologies and AI-assisted development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, or similar tools.

This is a Junior-level role for someone who is eager to build mobile applications, learn new technologies, and work on AI-enabled features.

Key Responsibilities

• Develop, maintain, and enhance cross-platform mobile applications using React Native.
• Build and support applications for both Android and iOS platforms.
• Develop reusable, scalable, and user-friendly mobile UI components.
• Integrate REST APIs, third-party APIs, SDKs, and backend services.
• Debug and troubleshoot applications across Android and iOS devices.
• Assist with application testing, performance optimization, and bug fixing.
• Assist in integrating AI/ML or GenAI-powered features into mobile applications.
• Use AI-assisted development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, or similar tools for development, debugging, code generation, and problem-solving.
• Collaborate with the development team to understand requirements and deliver features.
• Participate in code reviews and follow development best practices.

Required Skills

• 1–2 years of hands-on experience with React Native.
• Good hands on experience with JavaScript / TypeScript.
• Understanding of React Native components, hooks, navigation, and state management.
• Experience with REST APIs, JSON, and API integration.
• Exposure to Android and iOS application development.
• Familiarity with Android Studio and Xcode.
• Experience with mobile application debugging and testing.
• Exposure to AI/ML, GenAI, AI APIs, or AI-powered application features.
• Familiarity with AI-assisted development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, or similar tools.
• Good problem-solving and communication skills.

Educational Qualification

B.Tech / B.E. in CSE, IT, or AI/ML only.

Preferred Qualifications

• Candidates currently based in Hyderabad are preferred.
• Immediate Joiners or candidates with a short notice period are preferred.
• Exposure to AI/ML or GenAI projects is an advantage.
• Experience integrating AI APIs into mobile applications is a plus.
• Knowledge of Git/GitHub and basic CI/CD concepts is preferred.
• Experience with Google Play Store / Apple App Store deployment is an added advantage.

Work Location

Gachibowli, Hyderabad

Note: This is a Junior React Native Developer position. Candidates with 1–2 years of relevant React Native experience, Android/iOS exposure, and AI/ML exposure are encouraged to apply.

Responsibilities:

• Architect, develop, and maintain backend components for our Risk Decisioning Platform.
• Build and orchestrate scalable backend services that automate, optimize, and monitor high-value credit and risk decisions in real time.
• Integrate with ORM layers - such as SQLAlchemy - and multi-RDBMS solutions (Postgres, MySQL, Oracle, MSSQL, etc. ) to ensure data integrity, scalability, and compliance.
• Collaborate closely with Product Team, Data Scientists, QA Teams to create extensible APIs, workflow automation, and AI governance features.
• Architect workflows for privacy, auditability, versioned traceability, and role-based access control, ensuring adherence to regulatory frameworks.
• Take ownership from requirements to deployment, seeing your code deliver real impact in the lives of customers and end users.

Requirements:

• Proficiency in Python, SQLAlchemy (or similar ORM), and SQL databases.
• Experience developing and maintaining scalable backend services, including API, data orchestration, ML workflows, and workflow automation.
• Solid understanding of data modeling, distributed systems, and backend architecture for regulated environments.
• Curiosity and drive to work at the intersection of AI/ML, fintech, and regulatory technology.
• Experience mentoring and guiding junior developers.

Technical Skills:

• Languages: Python 3.9+, SQL, JavaScript/TypeScript, Angular.
• Frameworks: Flask, SQLAlchemy, Celery, Marshmallow, Apache Spark.
• Databases: PostgreSQL, Oracle, SQL Server, Redis.
• Tools: pytest, Docker, Git, Nx.
• Cloud: Experience with AWS, Azure, or GCP preferred.
• Monitoring: Familiarity with OpenTelemetry and logging frameworks.

Full Stack Developer – AI/ML & GenAI

Location: Mumbai

Experience: 7–9 Years

Employment Type: Full-time

Virtual Drive: Saturday

We are looking for a Python Full Stack Developer with 3+ years of hands-on experience in AI/ML and Generative AI.

Key Skills: Python Full Stack, FastAPI/Flask/Django, React/Angular, AI/ML, GenAI, LLMs, RAG, Embeddings, Vector Databases, Cloud (AWS/Azure/GCP), CI/CD, Docker & Kubernetes.

Role Overview

We are looking for an experienced Data Scientist – Agentic AI with strong expertise in Python, Machine Learning, Generative AI, Large Language Models (LLMs), RAG and Agentic AI.

The ideal candidate should have hands-on experience in developing, fine-tuning, evaluating and deploying machine learning and GenAI solutions. The candidate should be comfortable working with open-source LLMs, LangChain/LangGraph, PySpark and AI observability/tracing frameworks.

The role involves building intelligent AI systems that can reason, use tools, retrieve information and execute multi-step tasks using Agentic AI architectures.

Mandatory Technical Skills

1. Data Science & Python

• 5+ years of experience in Data Science / Machine Learning / AI.
• Strong programming experience in Python.
• Strong understanding of data analysis, feature engineering and statistical techniques.
• Experience with Python ML and data science libraries such as:
• NumPy
• Pandas
• Scikit-learn
• Matplotlib / Seaborn
• Good understanding of data preprocessing, exploratory data analysis and experimentation.

2. Machine Learning & Statistics

• Strong understanding of Machine Learning fundamentals.
• Experience with supervised and unsupervised learning techniques.
• Knowledge of:
• Regression
• Classification
• Clustering
• Feature Engineering
• Model Selection
• Hyperparameter Tuning
• Cross-validation
• Strong understanding of Statistics / ML fundamentals.
• Ability to interpret model performance and statistical results.

3. Generative AI / LLM

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Understanding of Transformer architecture and modern LLM-based applications.
• Experience working with commercial or open-source LLMs.
• Strong understanding of:
• Prompt Engineering
• Context Management
• Embeddings
• Tokenization
• LLM inference
• Hallucination mitigation

4. RAG – Retrieval Augmented Generation

• Strong hands-on experience developing RAG applications.
• Experience with:
• Document ingestion
• Chunking
• Embeddings
• Vector search
• Semantic search
• Retrieval pipelines
• Context retrieval
• Reranking
• Ability to optimize RAG pipelines for relevance, accuracy and latency.
• Experience integrating LLMs with enterprise knowledge sources.

5. Agentic AI

• Hands-on experience building Agentic AI / AI Agent solutions.
• Understanding of agent architecture and multi-step reasoning workflows.
• Experience with:
• AI Agents
• Multi-Agent systems
• Tool Calling
• Function Calling
• Agent orchestration
• Planning and reasoning workflows
• Memory
• Workflow automation
• Ability to build agents that can interact with tools, APIs, databases and external systems.

6. LangChain / LangGraph

• Strong hands-on experience with LangChain and/or LangGraph.
• Experience building LLM workflows and agent-based applications.
• Understanding of:
• Chains
• Agents
• Tools
• State management
• Graph-based workflows
• Agent orchestration
• Retrieval workflows
• Experience designing scalable Agentic AI workflows.

7. LLM Fine-Tuning

• Hands-on experience with LLM fine-tuning.
• Understanding of techniques such as:
• Supervised Fine-Tuning (SFT)
• Parameter-Efficient Fine-Tuning
• LoRA
• QLoRA
• Experience preparing datasets for fine-tuning.
• Ability to evaluate fine-tuned models against baseline models.
• Understanding of model optimization and inference considerations.

8. BERT / LLaMA / Open-Source LLMs

Experience working with one or more open-source / transformer-based models such as:

• BERT
• LLaMA / Llama
• Mistral
• Gemma
• Qwen
• Other open-source LLMs

Candidate should understand model loading, inference, fine-tuning and evaluation.

9. PySpark

• Strong experience with PySpark for large-scale data processing.
• Experience working with large datasets and distributed data processing.
• Knowledge of:
• Data transformations
• Data cleaning
• Aggregations
• Joins
• Spark SQL
• Performance optimization
• Ability to build scalable data processing pipelines.

10. Model Validation & Evaluation

• Experience validating and evaluating ML and GenAI models.
• Understanding of traditional ML evaluation metrics.
• Experience evaluating LLM/RAG applications using relevant quality metrics.
• Ability to compare model performance and identify areas for improvement.
• Experience with:
• Accuracy
• Precision
• Recall
• F1 Score
• ROC-AUC
• Retrieval metrics
• LLM response quality
• Groundedness / relevance
• Experience designing evaluation datasets and test cases is preferred.

11. AI Tracing / Observability

• Experience with AI/LLM tracing and observability.
• Ability to monitor AI applications in production.
• Experience tracking:
• LLM requests/responses
• Latency
• Token usage
• Errors
• Retrieval performance
• Agent/tool execution
• Model performance
• Exposure to tools/frameworks such as LangSmith, OpenTelemetry, Arize Phoenix, MLflow or similar is preferred.

12. Model Deployment

• Experience deploying ML/LLM/GenAI solutions into production.
• Exposure to cloud and/or on-premise model deployment.
• Experience with model serving, APIs and production inference.
• Knowledge of deployment environments such as:
• AWS
• Azure
• GCP
• On-premise infrastructure
• Experience with Docker, APIs and CI/CD is an advantage.

Key Responsibilities

• Design, develop and deploy Data Science, Machine Learning and GenAI solutions.
• Build production-ready RAG and Agentic AI applications.
• Develop intelligent agents capable of tool calling, reasoning and multi-step task execution.
• Build LLM-powered applications using LangChain/LangGraph.
• Work with open-source LLMs including BERT, LLaMA and other transformer-based models.
• Fine-tune LLMs for specific business use cases.
• Develop scalable data processing pipelines using PySpark.
• Perform data analysis, feature engineering and statistical modeling.
• Develop and maintain model validation and evaluation frameworks.
• Evaluate ML and LLM models using appropriate performance and quality metrics.
• Implement AI tracing, monitoring and observability for production GenAI systems.
• Deploy models and AI applications in cloud or on-premise environments.
• Optimize model performance, response quality, latency and cost.
• Troubleshoot issues related to model inference, retrieval, agents and LLM workflows.
• Collaborate with Data Scientists, ML Engineers, Software Engineers and business stakeholders.
• Convert business requirements into scalable AI/ML solutions.

Good to Have

• Experience with Vector Databases such as:
• FAISS
• Pinecone
• Weaviate
• Milvus
• Chroma
• Azure AI Search
• Experience with MLflow or similar ML lifecycle tools.
• Experience with Docker/Kubernetes.
• Experience with REST APIs / FastAPI.
• Knowledge of cloud AI/ML services.
• Experience with MLOps / LLMOps.
• Experience with multi-agent frameworks other than LangChain/LangGraph.
• Experience working with enterprise GenAI applications.

Ideal Candidate Profile

The ideal candidate should be a Data Scientist / ML Engineer with strong GenAI and Agentic AI experience, rather than a pure Python developer.

A strong candidate would typically have:

Data Science + Python + ML + Statistics + GenAI/LLM + RAG + Agentic AI + LangChain/LangGraph + LLM Fine-Tuning + Open-Source LLMs + PySpark + Model Evaluation + AI Observability + Model Deployment.

Core Mandatory Skills

Data Science, Python, Machine Learning, Statistics/ML Fundamentals, GenAI/LLM, RAG, Agentic AI, LangChain/LangGraph, LLM Fine-Tuning, BERT/LLaMA/Open-Source LLMs, PySpark, Model Validation/Evaluation, AI Tracing/Observability, Cloud/On-Prem Model Deployment.

🚀 WE’RE HIRING | AI/ML GENERATIVE AI ENGINEER

📍 Location: Remote

💼 Experience: 5+ Years

🔄 Position: Contract – Extendable

🔹 ROLE HIGHLIGHTS

➤ Build and deploy AI/ML amp; Generative AI solutions

➤ Develop LLM, RAG, NLP, Recommendation amp; Predictive solutions

➤ Work on AI Agents, Chatbots, Computer Vision amp; Content Intelligence

➤ Build ML models using PyTorch, TensorFlow, Keras amp; Scikit-learn

➤ Develop RAG solutions using LangChain, LlamaIndex, FAISS/Milvus

➤ Integrate OpenAI, Azure OpenAI, AWS Bedrock, Vertex AI amp; Hugging Face

➤ Build scalable AI APIs using Python, FastAPI/Flask/Django

➤ Contribute to Private AI amp; Smart Agentic Systems

⚙️ MUST-HAVE SKILLS

◆ Python – 3+ years

◆ AI/ML – 5+ years

◆ Generative AI – 2+ years

◆ LLMs, RAG, Embeddings , Transformers

◆ ML/DL, NLP amp; Predictive Analytics

◆ Cloud AI Platforms – Azure / AWS / GCP

◆ AI/ML Deployment | MLOps

Interview Process - F2F Round at Pune Location

Key Responsibilities

 Strong understanding of Machine Learning algorithms (supervised and unsupervised)

 Hands-on experience with Deep Learning frameworks (TensorFlow, PyTorch, or similar)

 Experience in NLP techniques and libraries (NLTK, spaCy, Hugging Face, etc.)

 Solid knowledge of Statistics, probability, and data analysis methods

 Proficiency in SQL for querying relational databases

 Strong programming skills in Python (or similar languages)

 Excellent communication skills with the ability to explain complex concepts simply

Required Qualifications

 3+ years of hands-on experience as a Data Scientist or in a similar role.

 Strong expertise in classical machine learning and regression modeling.

 Solid understanding of statistics, including probability, distributions, hypothesis testing,

and correlation analysis.

 Proficiency in Python with libraries such as: scikit-learn pandas NumPy

 Experience working with structured/tabular data.

 Strong problem-solving and analytical thinking skills.

 Ability to clearly explain models and results to non-technical stakeholders.

Role Overview

We are looking for an AI/ML Engineer with 2 - 6 years of industry experience to develop and deploy machine learning solutions for industrial and smart manufacturing use cases. The role involves working with real-world industrial data, developing predictive and analytical models, and translating business and operational requirements into scalable AI/ML solutions.

The ideal candidate should have strong fundamentals in machine learning, Python, data processing, model development, and deployment, along with an interest in applying AI to manufacturing, industrial automation, and operational optimization.

Key Responsibilities

• Design, develop, train, and evaluate machine learning models for industrial and manufacturing use cases.
• Analyze large and complex datasets to identify patterns, trends, anomalies, and opportunities for optimization.
• Perform data preprocessing, feature engineering, model selection, and performance evaluation.
• Develop solutions for use cases such as predictive maintenance, anomaly detection, quality prediction, process optimization, forecasting, and equipment monitoring.
• Work with structured and time-series data generated from industrial equipment, machines, sensors, and operational systems.
• Develop and optimize ML pipelines for data preparation, model training, validation, and deployment.
• Collaborate with domain experts, data engineers, software engineers, and business stakeholders to understand requirements and translate them into technical solutions.
• Deploy machine learning models into production environments and monitor model performance.
• Troubleshoot model and data-related issues and continuously improve model accuracy, reliability, and scalability.
• Develop reusable code, APIs, and components to integrate ML models with enterprise and industrial applications.
• Document models, methodologies, experiments, results, and technical implementations.
• Stay updated with emerging AI/ML techniques, industrial AI trends, and smart manufacturing technologies.

Required Technical Skills

• Strong programming experience in Python.
• Good understanding of Machine Learning algorithms and concepts.
• Experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy, and preferably TensorFlow or PyTorch.
• Strong understanding of data preprocessing, feature engineering, model training, validation, and evaluation.
• Experience working with time-series data is preferred.
• Good understanding of statistical concepts and data analysis.
• Experience with SQL and relational databases.
• Understanding of model deployment and productionization of ML solutions.
• Familiarity with Git and software development best practices.
• Good problem-solving and analytical skills.

Preferred Skills

• Experience in Industrial AI, Smart Manufacturing, Industry 4.0, or Industrial IoT (IIoT).
• Experience with predictive maintenance, anomaly detection, forecasting, or quality inspection.
• Exposure to sensor data, machine/equipment data, telemetry, or real-time industrial data.
• Knowledge of computer vision for manufacturing or quality inspection use cases.
• Exposure to cloud platforms such as AWS, Azure, or GCP.
• Familiarity with Docker, Kubernetes, or CI/CD for ML deployment.
• Knowledge of MLOps concepts and tools.
• Exposure to Generative AI/LLMs is an added advantage. 

• Bachelor’s degree in computer science, Data Science, Information Systems, or a related field  
• 8-10 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  

We are looking for a Data Science & Machine Learning Senior Associate with 3–5 years of relevant experience in data science, machine learning, and analytics. The candidate will be responsible for developing predictive models, analyzing complex datasets, building scalable ML solutions, and supporting production-grade data science applications on cloud platforms.

• Develop and implement machine learning and predictive analytics models.
• Perform data analysis, statistical modeling, and optimization to solve business problems.
• Build demand forecasting and predictive models using time-series and other advanced techniques.
• Work with large datasets using Python, SQL, PySpark, and cloud-based data platforms.
• Develop and maintain scalable data pipelines for ML model development and deployment.
• Implement MLOps practices including model deployment, monitoring, retraining, and data-drift detection.
• Collaborate with software engineers, product teams, and business stakeholders to convert business requirements into analytical solutions.
• Validate models for accuracy, robustness, bias, and production readiness.
• Create meaningful visualizations and communicate analytical insights to technical and non-technical stakeholders.

• Python
• SQL
• Data Science & Machine Learning
• Predictive Modeling
• Statistical Analysis
• Machine Learning Algorithms
• Optimization Techniques
• Google Cloud Platform (GCP)
• BigQuery
• Dataflow
• Dataproc
• Data Fusion
• Cloud SQL
• Airflow
• PySpark
• PostgreSQL
• Terraform
• Tekton
• APIs
• MLOps

• Java

• Demand Forecasting
• Time-Series Analysis
• Neural Networks
• Ensemble Methods
• Support Vector Machines (SVM)
• Regression and Cluster Analysis
• ML Model Testing
• Bias Detection and Data Drift Monitoring
• Production ML Deployment
• QlikSense
• Automotive or Supply Chain Analytics

Bachelor's degree in Computer Science, Data Science, Engineering, Statistics, Mathematics, or a related technical field.Master's degree in a relevant quantitative or technical field is preferred.

Lead Developer – Java, React, Node.js & AI/ML

Location: Mumbai

Experience: 8–10 Years

Job Type: Full-time

Job Description

We are looking for a Lead Developer with 8–10 years of experience in software development, with strong expertise in Java, React, and Node.js. The candidate should have at least 1 year of hands-on professional experience in Artificial Intelligence and Machine Learning and experience in building modern, scalable technology solutions.

Key Skills

Java, React, Node.js, AI/ML, MongoDB, PostgreSQL, Key-Value Caching, Vector Databases, ChromaDB, pgvector

Candidate should have strong software development experience along with good knowledge of modern databases, caching technologies, and vector search solutions.

Greetings!

Hiring For Large Product Based Company!

Role- Mlops Engineer

Experience- 8-12 years

Location- Pune, Nagpur

JD-

• 8-10 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  

We are looking for motivated and enthusiastic candidates for a Python and Machine Learning Internship. This internship provides an opportunity to gain practical experience in Python programming, data analysis, and machine learning through hands-on projects and industry-oriented tasks.

Key Responsibilities:

• Develop and implement Python-based applications and solutions.
• Work with data preprocessing, analysis, and visualization.
• Build, train, and evaluate machine learning models.
• Apply machine learning algorithms to real-world datasets.
• Assist in developing and testing project modules.
• Debug code and improve model performance.
• Work on practical projects under the guidance of experienced trainers/mentors.

Who Can Apply:

Pursuing students from BE/BTech, BCA, BSc, MCA, MTech, MSc, Diploma and related computer science backgrounds.

Mode of internship: Offline / Online / Hybrid

What You Will Gain:

• Hands-on experience with Python and Machine Learning.
• Practical project development experience.
• Exposure to real-world datasets and ML applications.
• Guidance from experienced mentors.
• Internship Certificate and Letter of Recommendation upon successful completion.

Hiring for Junior AI Engineer

Exp : 4 - 6 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune WFO

Skills :

- Min 3 years strong programming experience in Python is a MUST

- Min 2 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

- Experience with cloud platforms (AWS/Azure/GCP)

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

Experience: 0–1 Year / Freshers

Location: Remote

Employment Type: Full-Time

About the Role

We are looking for a Junior Software Engineer – AI/ML with hands-on academic, internship, or personal project experience in Core AI/ML.

Key Areas We’re Looking For

• Core AI/ML
• Trading / Finance
• CCTV / Video Analytics
• Face Recognition
• OMR / OCR

Required Skills

• Python and basic Machine Learning concepts
• Experience with AI/ML projects
• Knowledge of OpenCV, Scikit-learn, PyTorch, or TensorFlow
• Basic understanding of model training, evaluation, and data preprocessing
• Git/GitHub

Candidate Profile

• Freshers or candidates with 0–1 year experience
• Practical AI/ML projects preferred
• Should be able to clearly explain their project, model/algorithm, dataset, and individual contribution

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).

Bachelor’s degree in Engineering, Computer Science, or a related discipline

• 8+ years of experience in project or programme management

• 3+ years of experience delivering AI, Machine Learning, Data, or Analytics programmes

• PMP, Agile, Scrum, or equivalent certification preferred

Title                                 : Senior AI Platform / MLOps Engineer

Experience                    : 6+ years

Work type                      : Chennai - Work from Office/other locations - Remote

Employment Type      : Full Time

Notice Period              : Immediate

Work Day                     :Mon to Fri

Key Responsibilities:

• Install, configure and operate OpenShift, NVIDIA GPU operator, OpenShift AI, and NIM microservices on 12× RTX PRO 6000 across two servers; single-node and HA control-plane topologies
• Serving configuration and tuning: quantized model deployment (FP8/FP4), replica balancing, batching, KV-cache and context management
• Azure GPU build environments: provisioning, cost control, parity with the on-prem stack via pinned container/model versions; cloud-to-factory migration with parity regression
• GitOps CI/CD, container registry, artifact/model versioning, environment promotion; observability and audit wiring (Splunk, Prometheus/Grafana)
• Benchmark automation: load harness, p50/p95/p99 latency, tokens/sec, GPU utilization; the capacity report data pipeline
• Platform upgrade procedure with evaluation-regression gates; deployment runbook as a first-class deliverable

Technical Skills:

• 6+ years infrastructure/platform engineering with 3+ years production Kubernetes; OpenShift experience strongly preferred
• Hands-on GPU inference serving in production: NIM, Triton, vLLM, or TensorRT-LLM — you have sized, deployed, and tuned LLM serving on real GPUs and can talk memory-bandwidth trade-offs
• GitOps fluency (ArgoCD/Flux), infrastructure-as-code, container internals; comfortable in air-gapped/proxy-restricted enterprise networks
• Observability depth: metrics, traces, log pipelines; has built performance test harnesses, not just run them

• Azure or AWS GPU compute operations experience

Strongly preferred

• NVIDIA GPU operator and AI Enterprise stack specifics; KServe; Milvus or pgvector operations; VAST/NFS/S3 storage integration; banking or other regulated-environment delivery

About Ampera: 

Ampera Technologies, a purpose driven Digital IT Services with primary focus on supporting our client with their Data, AI / ML, Accessibility and other Digital IT needs. We also ensure that equal opportunities are provided to Persons with Disabilities Talent. Ampera Technologies has its Global Headquarters in Chicago, USA and its Global Delivery Center is based out of Chennai, India. We are actively expanding our Tech Delivery team in Chennai and across India. We offer exciting benefits for our teams, such as 1) Hybrid and Remote work options available, 2) Opportunity to work directly with our Global Enterprise Clients, 3) Opportunity to learn and implement evolving Technologies, 4) Comprehensive healthcare, and 5) Conducive environment for Persons with Disability Talent meeting Physical and Digital Accessibility standards 

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.

Hi,

Greetings !!

We/re are looking for someone who has Hands-on experience with CV/ML

The location for the same is Bangalore.

Requirements

• 11–14 years total experience
• Computer Vision – strong hands-on experience
• Object Detection – YOLO(Preferred), Faster R-CNN, SSD, etc.
• Image Processing – OpenCV, image enhancement, segmentation, feature extraction
• Machine Learning / Deep Learning – CNNs, model training, evaluation, optimization
• AI/ML – production-level AI solution development
• LLM / GenAI – practical exposure to LLMs, multimodal AI, RAG, VLMs, or GenAI
• Python – strong programming skills
• Model deployment – preferably TensorRT, ONNX, Docker, Kubernetes, cloud, or edge deployment
• Bangalore – candidate should be based in / willing to work from Bangalore

Preferred

• Vision Transformers / ViT
• YOLOv8/YOLOv9/YOLOv10/YOLO11
• PyTorch / TensorFlow
• NLP / LLM / VLM
• Generative AI
• CUDA / GPU optimization
• Edge AI / NVIDIA
• Experience leading CV/AI projects or teams

If interested, Share CV at: snigdhaattheratebeanhr.com

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

Responsibilities

• Engineering technical lead for the AI Garage team which responsible for developing AI/ML systems and Agentic solutions in HR domain 
• Drive engineering excellence for the AI Garage team 
• Provide technical leadership in regards to immediate and complex technical problems to leads of associated teams.
• Influence and Design and build scalable Agentic systems in HR domain. 
• Collaborate with AI Garage Leads ( Eng Managers, Product Managers and AI strategist ) and stakeholders to understand their needs and translate them into technical solutions.
• Stay up-to-date with the latest trends in Agentic and AIML engineering.

Required Skill :

• Master's degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience
• 8+ years of experience in software development, including proficiency in one or more programming languages, artificial intelligence, machine learning algorithms and tools, and Generative AI (Large Language Models, Multi-Model, etc.)
• Design and implement scalable AI solutions for Enterprises.Experience in Building and rolling out AI and agentic solutions for Global Enterprises
• Multi-Agent Systems: Handle collaboration, memory persistence, and dynamic planning using frameworks like LangGraph, CrewAI, or AutoGen.
• Optimization & Evaluation: Track agent trajectories, manage cost/latency, and implement LLM-as-a-judge frameworks (e.g., Phoenix, LangSmith).
• Safety & Compliance: Prompt injection mitigation, sandboxed code execution, and human-in-the-loop (HITL) checkpoints.
• Experience with offline,online and RL based validation
• Metrics Selection: Knowing which evaluation metric matches specific business constraints (e.g., using Precision/Recall or Area Under the ROC Curve (AUC) rather than basic Accuracy on an imbalanced classification dataset).
• Good understanding of the Bias-Variance tradeoff, Overfitting vs. Underfitting, and practical techniques to fix them (L1/L2 Regularization, Dropout, Early Stopping). 
• Dynamic Context Compaction: Skills in designing algorithms that programmatically truncate, summarize, or extract semantic milestones from a running conversation log so the agent doesn't suffer from "lost in the middle" phenomena during long-horizon tasks.
• Advanced Reasoning Frameworks: Implementing and customizing structural execution patterns like ReAct (Reason + Act), Tree of Thoughts (ToT), and Plan-and-Solve loops, giving models the cognitive scaffolding to break down vague goals into deterministic DAGs 
• Evidence in runtime layers that scan user inputs for prompt injection or jailbreak attempts designed to hijack an agent’s system tools, alongside strictly isolating tool execution environments
• Experience with LoRA or SFT and reasoning 

Job Summary/ Job Opportunity:

This is an excellent opportunity for an ideal candidate with a high level of technical proficiency and meeting the below mentioned criteria -- • Strong experience in Machine Learning, Deep Learning, Generative AI, and Large Language Models (LLMs). • Hands-on experience building and deploying production-grade solutions using Azure OpenAI, OpenAI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, and Agentic AI frameworks. • Strong expertise in Python, API development, microservices, and cloud-native architectures. • Experience designing and implementing RAG solutions, vector databases, embeddings, knowledge retrieval systems, and AI copilots. • Experience with Azure cloud services, MLOps, CI/CD pipelines, monitoring, and model lifecycle management. • Strong understanding of AI governance, responsible AI, security, compliance, and model evaluation frameworks. • Ability to lead technical discussions, provide architectural recommendations, mentor team members, and interact with business stakeholde

Key Objectives and Major Responsibilities:

• Design, develop, and implement scalable AI/ML and Generative AI solutions for enterprise applications. • Lead development of intelligent applications leveraging LLMs, RAG pipelines, AI agents, and document intelligence solutions. • Collaborate with business stakeholders, architects, and product teams to translate business requirements into technical solutions. • Design and optimize data pipelines, vector search solutions, embeddings, and retrieval mechanisms. • Build and maintain REST APIs, microservices, and cloud-native AI applications. • Ensure best practices in coding standards, performance optimization, security, scalability, and maintainability. • Drive AI solution deployment using MLOps practices, CI/CD pipelines, monitoring, and observability frameworks. • Perform code reviews, mentor junior developers, and contribute to capability building within the team

Key Capabilities and Competencies:

Knowledge, Skills, Qualification and Experience

• Degree in B.Tech/M.Tech (Computer Science/IT/Data Science) or related discipline preferred, with 3–4 years of relevant experience in AI/ML, GenAI and total 5-7 years of experience. • Proficiency in Python and hands-on experience with ML libraries (scikit-learn, TensorFlow, PyTorch) and GenAI frameworks/tools. • Strong understanding of machine learning, deep learning, LLMs, prompt engineering, and techniques like RAG and fine-tuning. • Experience with data processing, embeddings, vector databases, APIs, and building scalable AI driven applications. • Good communication skills, ability to work on multiple projects, and eagerness to learn and adapt to evolving AI technologies. 

About Us

Corporate Web Solutions provides technology and digital transformation solutions with an emphasis on emerging technologies including artificial intelligence and machine learning.

Role Overview

As an AI & Machine Learning Intern, you'll gain practical experience working with datasets, machine learning models, AI concepts, and Python-based tools.

Key Responsibilities

• Prepare and analyze datasets.
• Assist in developing machine learning models.
• Experiment with AI and ML algorithms.
• Evaluate model performance.
• Work with Python and relevant libraries.
• Participate in practical AI/ML projects.

Requirements

• Basic Python programming knowledge.
• Understanding of machine learning fundamentals.
• Basic knowledge of data handling.
• Familiarity with NumPy and Pandas is a plus.
• Eagerness to learn AI technologies.

Perks

• Certificate of Internship
• Flexible work hours
• Mentorship and real project exposure
• Potential for PPO
• Letter of Recommendation
• Performance-Based Stipend available up to ₹18,000/month

Data Science (SME)

FACE Prep is India’s leading employability and industry-aligned education company. For over 17 years, we have partnered with higher education institutions to bridge the gap between academia and industry.

● Worked with 2,000+ colleges and universities across India

● Trained 6+ million students across disciplines

● Market leaders in:

● Employability skills & placement readiness

● Industry-aligned academic papers(credit-bearing)

● Full-scale industry-aligned degree programs with universities

● Pan-India operations, headquartered in Coimbatore

● Young, energetic, mission-driven team

● Fast-growing organization with headcount expected to cross 1,000 employees in the next financial year

At FACE Prep, people are the platform on which outcomes are built.

Position: Data Science (SME)

Location: Coimbatore

Duration: Full-Time

Department: Training & Development

Experience: Minimum 2 Years

Role Description :

The Data Science Subject Matter Expert (SME) is a full-time, on-site role based in Coimbatore. The SME will design and deliver data science curricula, including lectures, hands-on labs, and project-based learning for students preparing for tech careers. Daily responsibilities include creating educational content, developing problem sets and case studies, mentoring learners on data analysis and model-building, and reviewing student work to provide constructive feedback. The SME will collaborate with other trainers and the product team to align content with current industry practices and hiring requirements. The role also involves analyzing learner performance data, refining teaching strategies, and occasionally contributing to workshops, bootcamps, and online learning materials.

Requirements:

• Strong foundation in Statistics and Data Science, with the ability to explain complex concepts clearly and practically.

• Hands-on experience in Data Analytics and Data Analysis, including working with real-world datasets and deriving actionable insights.

• Excellent Analytical Skills for problem-solving, designing assignments, and guiding learners through data-driven decision-making.

• Proficiency in common data science tools and languages (e.g., Python/R, SQL, data visualization tools such as Tableau/Power BI).

• Prior experience in teaching, mentoring, corporate training, or curriculum development is highly beneficial.

• Bachelor’s or Master’s degree in a quantitative field such as Computer Science, Statistics, Mathematics, Data Science, or related discipline.

• Ability to communicate effectively with diverse learner groups and collaborate within a multidisciplinary team.

• Exposure to machine learning concepts, model evaluation, and deployment practices is a plus.

IMMEDIATE JOINERS REQUIRED

Required Skills:-

• 4+ years of experience in Software Testing (Manual & Automation).
• Strong experience with Selenium or Playwright.
• Hands-on experience in API Testing (Postman, REST Assured, Swagger, etc.).
• Experience with CI/CD pipelines (Jenkins, GitLab CI, Azure DevOps, GitHub Actions).
• Strong understanding of Functional, Regression, and Non-functional Testing.
• Excellent analytical and debugging skills.

AI-Specific Skills

• Hands-on experience in GenAI / AI Testing.
• Experience testing LLM-based applications.
• Strong understanding of prompt engineering and prompt validation.
• Knowledge of LLM behavior, model variability, and non-deterministic outputs.
• Experience validating AI outputs for accuracy, hallucinations, bias, and safety.
• Test data management for AI applications.
• Understanding of Responsible AI testing concepts.

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

About the Role

We are looking for a dynamic and customer-focused Presales Engineer – Cloud & AI to support our Cloud Transformation, Modernization, Managed Services, and AI/GenAI practices.

This role is pivotal in shaping technical solutions for prospective customers, driving value-based conversations, and bridging the gap between customer challenges and Comprinno's cloud and AI capabilities.

As a Presales Engineer – Cloud & AI, you will work closely with Sales, Solution Architects, Data & AI teams, and Delivery teams to understand customer requirements, design high-level solutions, and articulate how Comprinno's offerings can accelerate digital transformation and business outcomes.

This is a high-impact role that combines cloud solutioning, technical consulting, AI awareness, customer engagement, and presales excellence.

Key Responsibilities

Customer Discovery & Solutioning

• Engage with prospective customers to understand business objectives, technical challenges, and transformation goals.
• Conduct discovery workshops, assessments, and technical discussions to identify cloud and AI opportunities.
• Translate business requirements into high-level cloud and AI solution architectures.
• Collaborate with Solution Architects and technical teams to develop scalable and cost-effective solutions.

Cloud Transformation Consulting

• Support customer initiatives across:
• Cloud Migration
• Application Modernization
• DevOps Transformation
• Managed Services
• Security & Compliance
• Resilience & Disaster Recovery
• Conduct cloud assessments and migration readiness evaluations.
• Prepare cloud cost estimates, modernization roadmaps, and optimization recommendations.
• Leverage AWS programs and frameworks such as:
• Migration Acceleration Program (MAP)
• AWS Well-Architected Framework (WAFR)
• Foundational Technical Review (FTR)

AI & GenAI Solution Consulting

• Identify opportunities for AI, GenAI, and Agentic AI adoption within customer environments.
• Participate in AI discovery workshops and business value discussions.
• Support solution design for AI-powered applications leveraging AWS AI services.
• Collaborate with Data & AI teams to position analytics, machine learning, and Generative AI offerings.
• Assist customers in identifying suitable use cases for:
• Generative AI
• AI Assistants
• Knowledge Management Solutions
• Intelligent Automation
• Conversational AI

Proposal Development & Technical Presales

• Collaborate with Solution Architects to create:
• Technical Proposals
• Architecture Diagrams
• Effort Estimates
• Statements of Work (SOWs)
• Technical Responses to RFPs/RFIs
• Present solutions, recommendations, and technical approaches to customer stakeholders.
• Support Proof of Concepts (PoCs), demonstrations, and technical validations.
• Participate in technical reviews and solution walkthroughs.

Customer Engagement & Thought Leadership

• Attend customer meetings jointly with Sales teams to provide technical expertise.
• Build credibility and trust with customer technical and business stakeholders.
• Contribute to webinars, workshops, technical presentations, and customer-facing events.
• Write blogs, case studies, solution briefs, and technical content that supports business growth.
• Maintain an active presence in cloud and AI communities.

Collaboration & Knowledge Management

• Collaborate with Delivery teams to ensure smooth handover and alignment between presales and execution.
• Maintain reusable presales assets, templates, assessment frameworks, and knowledge repositories.
• Continuously stay updated on AWS services, AI advancements, partner programs, and emerging technology trends.
• Contribute to internal capability building and knowledge-sharing initiatives.

Travel & Customer Interaction

• Travel as required for customer meetings, workshops, assessments, events, and strategic engagements.
• Represent Comprinno professionally during all customer-facing interactions.

Required Qualifications & Skills

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• 3–5 years of experience in Presales, Solution Consulting, Cloud Consulting, Technical Sales, or related customer-facing roles.
• Strong understanding of AWS cloud services across:
• Compute
• Networking
• Storage
• Security
• Databases
• Knowledge of cloud migration and modernization frameworks such as AWS MAP.
• Familiarity with AWS cloud assessment tools including Migration Evaluator and AWS Well-Architected Tool.
• Understanding of:
• Kubernetes
• Containerization
• Cloud-Native Architectures
• DevOps Practices
• Hands-on exposure to Infrastructure as Code using Terraform or CloudFormation.
• Ability to create:
• Solution Documents
• Architecture Diagrams
• Technical Proposals
• Effort Estimates
• Statements of Work
• Understanding of Generative AI concepts including:
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Assistants
• Agentic AI Concepts
• Familiarity with AWS AI services such as:
• Amazon Bedrock
• Amazon SageMaker
• Amazon Q
• AWS Solutions Architect – Professional Certification.
• Experience positioning AI, GenAI, Analytics, or Data Engineering solutions.
• Familiarity with AI frameworks such as:
• LangChain
• LangGraph
• CrewAI
• OpenAI APIs
• Anthropic APIs
• Strong communication, presentation, and stakeholder management skills.
• Experience conducting AI assessments, GenAI workshops, or AI proof-of-concepts.
• Experience contributing to webinars, blogs, case studies, or technical marketing content.
• AWS Solutions Architect – Associate Certification (Mandatory).
• Customer-centric mindset with strong problem-solving abilities.

Desired Qualifications & Skills

• AWS Specialty Certifications such as:
• Security Specialty
• Advanced Networking Specialty
• Machine Learning Specialty
• DevOps Engineer Professional
• Understanding of compliance frameworks such as:
• ISO 27001
• SOC 2
• HIPAA
• MBA or equivalent business qualification.
• Contributions to industry blogs, webinars, conferences, technical communities, or open-source initiatives.

What We're Looking For

• Strong consulting mindset with the ability to understand customer business challenges.
• Excellent communication and presentation skills.
• Ability to balance technical depth with business value articulation.
• Passion for cloud technologies, AI, and emerging innovation.
• Collaborative team player who can work across Sales, Architecture, Delivery, and Product teams.
• High ownership and accountability.
• Curiosity and continuous learning mindset.
• Willingness to travel and work from customer locations across India and internationally as required by project engagements.

What Success Looks Like

• Consistently contributes to qualified pipeline creation and deal progression.
• Produces high-quality proposals, architecture recommendations, assessments, and Statements of Work.
• Builds credibility with customers through strong technical consulting and solution design.
• Successfully supports closure of Migration, Modernization, Managed Services, Security, and AI opportunities.
• Develops reusable presales assets, assessment frameworks, and solution accelerators.
• Contributes to thought leadership through blogs, webinars, technical presentations, and customer-facing content.
• Continuously expands expertise in AWS, Cloud, AI, and emerging technologies.

Why Join Comprinno

• Work at the forefront of Cloud, AI, and AWS innovation with one of the leading AWS Partners in APJ.
• Be part of real transformation initiatives involving Migration, Modernization, DevOps, Security, Managed Services, and Generative AI.
• Collaborate directly with enterprise customers and influence strategic technology decisions.
• Accelerate your growth through AWS certifications, mentorship, and hands-on customer engagements.
• Contribute to innovative solutions including Tevico and future AI-powered offerings.
• Build thought leadership through webinars, blogs, events, and community participation.
• Join a collaborative, high-performance culture that values ownership, innovation, and continuous learning.

AuxoAI is seeking a Senior Applied Scientist to design and deploy structured knowledge systems that enable reliable, schema-grounded AI and agent reasoning.

This role sits at the intersection of large language models, knowledge graphs, semantic architectures, and hybrid retrieval systems. The ideal candidate will build systems that transform unstructured data into structured knowledge representations, enforce semantic constraints, and enable hybrid symbolic–neural reasoning in production environments.

You will play a key role in designing scalable semantic infrastructures that support advanced AI use cases such as GraphRAG pipelines, structured extraction, and agent reasoning workflows.

You will work on problems where existing architectures may not be sufficient and will experiment with new approaches that combine machine learning, knowledge graphs, semantic constraints, and classical AI techniques to build reliable, production-grade systems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

• Design schema-guided information extraction systems using zero-shot and few-shot structured prompting, constrained decoding approaches such as JSON schema enforcement or grammar-based decoding, and function-calling or tool-driven extraction techniques.
• Develop recursive or multi-stage extraction pipelines capable of handling nested entities, hierarchical structures, and cross-document relationships.
• Build ontology-driven systems using frameworks such as LinkML, OWL, SHACL, or similar schema modeling tools, and implement knowledge representations using RDF triples or labeled property graphs.
• Design and optimize entity resolution algorithms using techniques such as blocking strategies, embedding similarity, and rule-based matching.
• Develop ontology alignment techniques and graph embedding models such as Node2Vec or TransE-style approaches where appropriate.
• Design hybrid retrieval architectures combining dense vector retrieval, sparse retrieval techniques, and graph traversal algorithms such as BFS, DFS, path ranking, and neighborhood expansion.
• Build validation systems that enforce schema conformance, detect semantic inconsistencies, and reduce hallucinated or invalid structured outputs.
• Integrate structured knowledge systems into GraphRAG pipelines, agent planning frameworks, and tool-selection workflows.
• Deliver production-grade semantic systems with clear targets for latency, scalability, reliability, and data integrity.

Requirements

• 5+ years of experience building production AI or machine learning systems.
• Strong experience designing and implementing knowledge graphs or ontology-driven architectures.
• Hands-on experience implementing structured extraction techniques, including grammar-constrained decoding, JSON schema enforcement, or AST-style parsing approaches.
• Experience building entity resolution systems beyond simple embedding similarity methods.
• Experience working with graph query languages such as SPARQL or Cypher and optimizing graph query performance.
• Familiarity with RDF, OWL, or property graph data models and semantic data architectures.
• Strong Python engineering skills, with emphasis on data validation, schema integrity, and system reliability.
• Experience designing hybrid symbolic and neural AI systems.

Nice to Have:

• Experience implementing graph algorithms such as PageRank, community detection, or shortest-path algorithms for reasoning chains.
• Experience building graph-enhanced retrieval systems such as GraphRAG.
• Experience designing compositional semantic extraction pipelines.
• Experience implementing reasoning engines or rule-based inference systems.
• Experience benchmarking and evaluating structural extraction accuracy and consistency.

AuxoAI is hiring a Senior Applied Scientist to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.

This role focuses on building reasoning and decision systems using planning algorithms, search methods, and optimization techniques, rather than chatbot or RAG-style application development. The ideal candidate will design intelligent agent architectures that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques, operating reliably in real-world environments with constraints around latency, cost, uncertainty, and limited context windows.

You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems.

You will also work on problems where existing architectures may not be sufficient, and will be expected to experiment with new approaches that combine machine learning, graph algorithms, and classical AI techniques to build reliable, production-grade systems.

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)

Responsibilities:

• Design and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.
• Implement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches* to support complex decision-making tasks.
• Develop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.
• Build structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimized retrieval strategies.
• Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.
• Develop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.
• Improve agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.
• Deliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.

Requirements

• 3-10 years of experience building machine learning or AI systems in production environments.
• Strong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.
• Hands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.
• Strong understanding of state-space representations, heuristic design, and decision boundary trade-offs.
• Experience building or extensively customizing agent frameworks for real-world applications.
• Hands-on experience designing tool-use or function-calling architectures under practical system constraints.
• Strong Python engineering skills with a focus on scalable and reliable system design.
• 
  

Candidates whose primary experience is limited to RAG pipelines, prompt engineering, or chatbot frameworks without deeper algorithmic or systems work may not be a fit for this role.

Nice to Have:

• Experience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.
• Experience building multi-agent or collaborative agent systems.
• Experience designing evaluation frameworks for agent robustness and reliability.
• Experience optimizing LLM inference pipelines for latency, throughput, and cost efficiency.
• Familiarity with distributed task orchestration systems and large-scale AI workflow management.

AuxoAI 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. 

Location - Mumbai/Bangalore/Hyderabad/Gurgaon (Hybrid - 3 Days a week in Office)​​

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 

Requirements

• 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 

Sr.Data Scientist,Python, AI ML

We are looking for a skilled Data Scientist to analyze complex datasets, develop predictive models, and generate actionable insights that support business decisions. The ideal candidate should have strong statistical, analytical, and programming skills, along with hands-on experience in machine learning.

SRE / Success Engineering role focused on production operations, reliability, AWS infrastructure, monitoring, incident management, and platform support for the ZT platform.

Core responsibilities include:

• Production monitoring and debugging of live systems.
• Incident investigation, troubleshooting, and problem resolution.
• AWS cloud infrastructure support and maintenance.
• Deployment and operational support activities.
• Supporting a 24x7 production environment.
• Working with GitHub-based development workflows.
• Technical debt remediation and platform improvements.
• Customer issue investigation and support.
• Security and compliance-related work, including FedRAMP initiatives.

Preferred Skills:

AWS (especially S3 and EC2)

Strong debugging and troubleshooting skills

Site Reliability Engineering (SRE) experience

GitHub experience

Basic software development skills

TypeScript/JavaScript knowledge

C# preferred

AI experience is a plus.

Candidate should be a hands-on engineer with strong AWS, SRE, operational ownership, production support, and debugging capabilities, rather than a pure application or full-stack developer.

About the Role We are seeking a highly technical, hands-on Senior AI/ML Tech Lead to drive the design, development, and deployment of cutting-edge Generative AI applications. In this dual-impact role, you wi l act as a primary individual contributor architecting core AI engines while simultaneously leading a team of engineers through task alocation, code reviews, and technical mentorship. The ideal candidate bridges the gap between state-of-the-art AI research (LLMs, Agentic frameworks, Advanced RAG, OCR) and production-grade ful-stack engineering (Python, FastAPI, React).

Key Responsibilities

Technical Leadership & Team Management (40%)

● Technical Oversight: Lead a team of AI, backend, and ful-stack engineers; alocate tasks, establish sprint priorities, and ensure timely delivery.

● Code Quality & Reviews: Conduct rigorous code reviews to maintain high engineering standards, security, performance, and scalability across AI and fu l-stack codebases.

● Architecture & Governance: Design end-to-end system architectures for AI solutions, ensuring seamless integration between frontend interfaces, backend APIs, and AI models.

● Mentorship: Guide and upskil team members on modern software practices, LLM engineering, and agentic design patterns. Hands-On Engineering & Development (60%)

● Generative AI & Agentic Systems: Architect, build, and optimize LLM-powered applications, multi-agent workflows (e.g., CrewAI, AutoGen, LangGraph), and autonomous AI agents.

● RAG & OCR Pipelines: Design and deploy advanced RAG (Retrieval-Augmented Generation) architectures and document processing pipelines utilizing OCR techniques (e.g., LayoutLM, PaddleOCR, Tesseract, Vision LLMs) to extract structured data from unstructured sources.

● Backend Systems: Build robust, asynchronous, high-throughput microservices and RESTful APIs using Python and FastAPI.

● Frontend Integration: Colaborate on or build modern web interfaces using React (e.g., Control Towers, operations dashboards, interactive chat interfaces).

● MLOps & Vector DBs: Oversee model deployment, prompt engineering, fine-tuning, vector database integration (Pinecone, Qdrant, Chroma, PGVector), and cloud infrastructure setup (Azure/AWS).

Required Qualifications & Skills

● Overall Experience: 8 to 10 years of professional software engineering experience.

● AI/ML Domain Experience: 3 to 4+ years of dedicated, hands-on experience building and deploying AI/ML, OCR, and Generative AI solutions in production.

● Core Technical Stack: ○ Generative AI & LLMs: Extensive experience with commercial and open-source LLMs (OpenAI, Anthropic Claude, Llama), Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI), and LLM evaluation frameworks (LangSmith, TruLens, Ragas). ○ RAG & Unstructured Data: Strong knowledge of hybrid search, re-ranking, chunking strategies, vector databases, and document inte ligence workflows. ○ OCR & Vision Techniques: Hands-on experience with OCR engines (Tesseract, PaddleOCR, Azure Document Inteligence) and Multi-Modal/Vision LLMs for document extraction. ○ Backend: Deep expertise in Python and asynchronous frameworks (FastAPI, AsyncIO). ○ Frontend: Working proficiency in React (TypeScript/JavaScript) for building interactive web UI components. ○ Cloud & DevOps: Hands-on experience with cloud platforms (Azure / AWS), Docker, Kubernetes, and CI/CD pipelines.

Preferred / Good-to-Have Skills

● Experience with cloud-native data platforms (e.g., Microsoft Fabric, Snowflake, Azure SQL).

● Familiarity with cost optimization and latency reduction techniques for LLM inference (caching, semantic routing, model quantization).

● Prior experience in client-facing technical leadership or agile consulting environments.

What We Offer

● Opportunity to lead and build high-impact, state-of-the-art Generative AI systems.

● Colaborative engineering culture with room for technical ownership and direct business impact.

● Flexible work arrangements and competitive compensation package.

About the role

We are building AI systems that read, understand and act on real business documents, bank statements, financial reports, policy documents and forms and putting them into production where accuracy and cost both matters.

This is not a research role and it is not a prompt-writing role. You will own features end to end: pick and deploy open-source models, build the pipelines around them, measure whether they actually work on our documents, drive the cost per document down, and keep the whole thing running in production.

You will work closely with the engineering and product teams, and your work will be directly used by business users from day one.

What you will do

Deploy and evaluate open-source models

• Select, deploy and benchmark open-source LLMs and vision-language models for specific, narrow use cases not general chat.
• Build evaluation sets from real documents and define what "good" means numerically (field-level accuracy, extraction recall, hallucination rate) before shipping.
• Run structured comparisons between models and approaches, and write up the trade-offs so the team can make a decision.
• Apply quantization, batching and other optimizations to fit models into a sensible GPU budget.

Build and optimize AI orchestration

• Design multi-step pipelines that combine deterministic code, ML models and LLM calls and know when not to use an LLM.
• Optimize for latency, cost and reliability: caching, batching, request routing, fallback tiers, retries and graceful degradation.
• Instrument pipelines so failures are visible and traceable rather than silent.

Ship to production

• Package models and services with Docker, expose them behind clean APIs, and deploy them to our GPU and CPU infrastructure.
• Handle the unglamorous production concerns: cold starts, timeouts, concurrency limits, versioning, rollback and monitoring.
• Own on-call-style responsibility for the AI features you build, including cost tracking.

Must-have skills

Programming & engineering

• Strong Python: type hints, async/await, dataclasses/Pydantic, clean module design, testing.
• REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
• Git, code review discipline, and the ability to write code someone else can maintain.
• Comfortable in Linux and on the command line.

Machine learning fundamentals

• Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, tokenizers, accelerate).
• Understanding of inference-time concepts: tokenization, context windows, batching, precision (FP16/BF16/INT8), memory footprint.
• Ability to read a model card and a paper well enough to judge whether a model fits a use case.

Document processing

• Hands-on experience with at least two of: pypdfium2, PyMuPDF, pdfplumber, pdfminer.six, Docling, Unstructured, Surya, DocTR, LayoutLM family.
• Practical OCR experience (Tesseract, PaddleOCR, or a cloud OCR) and an understanding of when OCR is the wrong tool.
• Experience extracting tables from PDFs and dealing with merged cells, multi-line rows, and inconsistent column layouts.

Strongly preferred

You will be a much stronger candidate with any of these. We do not expect all of them.

Model serving & optimization

• vLLM, TGI, Ollama, llama.cpp, or Triton Inference Server.
• Quantization formats and tooling: GGUF, AWQ, GPTQ, bitsandbytes, ONNX Runtime, INT8 export.
• Serverless GPU platforms: Modal, RunPod, Replicate, Baseten including cold-start and container-lifecycle management.
• LoRA / QLoRA fine-tuning with PEFT for narrow, task-specific improvements.

Vision-language models

• Practical use of open VLMs: Qwen2.5-VL, InternVL, Granite Vision, Molmo, Phi-Vision, or similar.
• Awareness of where VLMs hallucinate especially on numeric and financial content and patterns for constraining them (using the model for layout only, sourcing values from the text layer, constrained decoding).

Orchestration & pipelines

• Workflow orchestration: Dagster, Airflow, Prefect, or Temporal.
• Async job patterns: Celery, RQ, or platform-native spawn/poll patterns.
• LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
• Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.

Evaluation & observability

• Building golden datasets and regression suites for extraction tasks.
• Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
• LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.

Nice extras

• Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
• Experience in fintech, lending, insurance or accounting documents.
• Handling of PII and data-security practices in document pipelines.
• Contributions to open-source ML or document-processing projects.

Why join us

• Real production ownership from month one your work goes to actual users, not a demo.
• Genuinely hard technical problems in document AI, not wrappers over an API.
• Small team, short decision cycles, direct access to leadership.
• Budget and freedom to evaluate and adopt new open-source models as they land.

To apply: send your CV along with a short note on one AI system you have taken to production what it did, what the accuracy was, and what broke.

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

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
• Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
• CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
• Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
• Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable
• Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
• Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
• Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
• Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
• Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
• Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

Get to Know Us:

At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together.

Equal Opportunity Employer:

Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. 

If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

Job application link : https://grnh.se/z7qx2ehx1us

About Us

Invorto is our Voice AI product, bringing intelligent voice agents to real-world customer and operational use cases. Our voice pipeline is built in Python, running an STT → LLM → TTS architecture on top of the Pipecat framework.

This is a chance to work on hard problems in voice AI — latency, accuracy, naturalness, and reliability — building zero-to-one, owning your area end-to-end, and shipping to production at scale.

Note: This is a customer-facing role, and strong communication skills are essential.

About the Role

We're looking for a Voice AI Research Engineer to join the Invorto team and help build and continuously improve the voice AI systems that power our intelligent voice agents. This role is focused on the specialized craft of voice AI — designing evaluation and automation frameworks that ensure our STT, LLM, and TTS pipeline performs reliably in real-world, production conditions.

What You'll Do

• Design and build automated testing and quality frameworks for our STT → LLM → TTS voice pipeline, built on Pipecat
• Evaluate and benchmark STT, LLM, and TTS/ASR components on accuracy, latency, naturalness, and robustness across accents, languages, and real-world audio conditions
• Work hands-on with STT, TTS, and ASR models — fine-tuning, evaluating, and improving them for production use cases
• Identify failure modes and edge cases across the pipeline (background noise, accents, interruptions, turn-taking, latency, pipeline-stage handoffs) and build systems to catch them before production
• Collaborate closely with engineering to integrate quality checks and automation into the voice agent development lifecycle within the Pipecat-based architecture
• Research and stay current with advances in voice AI, and bring in new techniques, models, and tools to improve pipeline performance
• Work directly with customers to understand real-world voice use cases and translate them into evaluation criteria and quality benchmarks
• Partner with product and engineering to define what "production-grade quality" means for voice agents and drive the team toward it

What We're Looking For

• 4–6 years of experience, with a specialization in voice AI systems and automated quality evaluation
• Hands-on experience with STT (Speech-to-Text), TTS (Text-to-Speech), and ASR (Automatic Speech Recognition) models
• Experience designing and building automated testing/evaluation frameworks for voice or speech systems
• Strong understanding of what drives voice AI quality — accuracy, latency, naturalness, and robustness to real-world variability
• Strong programming skills in Python; familiarity with Pipecat or similar voice pipeline/orchestration frameworks is a plus
• Understanding of STT → LLM → TTS pipeline architectures and the trade-offs involved at each stage
• Research mindset — comfortable exploring new models, techniques, and tools and translating them into practical improvements
• Excellent communication skills — this is a customer-facing role, and you'll regularly engage directly with customers to understand needs and validate quality expectations

Roles & Responsibilities

• Design and develop intelligent AI-based applications using advanced NLP and LLM techniques to solve real-world business challenges in financial services.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines leveraging structured and unstructured financial data.
• Integrate and orchestrate LLMs/SLMs for question-answering, summarization, semantic search, and document understanding.
• Develop and maintain RESTful APIs (sync and async) to serve NLP models and chatbot interfaces using frameworks like FastAPI, Flask, etc.
• Should have knowledge of advanced prompting techniques.
• Implement semantic search, hybrid search, and text retrieval systems using Elasticsearch and vector databases (e.g., FAISS, Pinecone, Weaviate).
• Perform NLP tasks such as entity recognition, text classification, intent detection, embedding generation, and sentiment analysis where required.
• Monitor and fine-tune LLM/SLM performance with real-world user data to improve relevance, latency, and accuracy.
• Exposure to LLMOps tools for monitoring, evaluation, and versioning of AI models in production.
• Build, train, and evaluate deep learning models for NLP tasks including classification, NER, summarization, and embedding generation.
• Develop traditional machine learning models (e.g., regression, decision trees, clustering) for structured data analysis and prediction tasks.
• Interact with cross-functional teams to understand system issues and follow up with respective teams to get them fixed.
• Understand and identify areas of improvement across businesses and participate in solution identification and implementation.
• Should be able to work as an Individual Contributor on new and existing projects.
• Positive and problem-solving attitude, must work as an independent contributor.

Ideal Candidate

1.Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

2.Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

3.Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support

4.Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

5.Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

6.Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models

.7.Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

8.Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

9.Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

10.Mandatory (CTC) - The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

11.Preferred (Experience 1) - Experience with LLMOps/MLOps tools for monitoring, evaluation, experimentation, and versioning of AI models.

12.Preferred (Experience 2) - Exposure to Azure OpenAI, Azure Kubernetes Service (AKS), Kubernetes, cloud-native AI deployments, or distributed systems.

13.Preferred (Experience 3) - Experience working with PostgreSQL, MongoDB, Redis, Kafka, or large-scale data platforms.

14.Preferred (Experience 4) - Familiarity with Docker, Kubernetes, cloud platforms, and scalable deployment architecture.

15.Preferred (Company) - Candidates from AI-first startups, product companies, SaaS organizations, fintech, or data-driven technology companies.

16.Mandatory ( Age ) - Candidate Should be Below 28 Years.

17.Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

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).

Are you interested in writing agentic systems that helps companies like Coca-Cola, ITC and Lenovo drive E-commerce success? Do you want to bring Autonomy to E-commerce? Then read on and apply.

Applied Scientist - Decision AI

Location: Bengaluru

Work Schedule: Hybrid (Candidate must be based in Bengaluru - 1-2 days of WFO may be required at a later date)

About Kily

Kily is an AI company bringing autonomy to digital commerce growth. We build autonomous agents that manage Advertising, Pricing and Listings for brands and sellers across commerce marketplaces.

Performance in modern commerce shifts constantly - across marketplaces, categories and cities - faster than teams can manually track, diagnose and act on. Kily's agents work continuously against real business objectives with each brands unique context, objectives and operating constraints and keeping humans in the loop where it matters.

The Role

We are looking for an Applied Scientist to build the models and decision systems behind Kily's recommendations and actions. The work is grounded in messy, real-world commerce data help build Kily's core decision intelligence layer: systems capable of understanding complex commerce data, determining why performance is changing, deciding what should be done about it, and ultimately taking actions autonomously at scale. You will work at the intersection of learning algorithms, decision making under uncertainty and agentic systems.

What You'll Do

·      Conduct deep analysis of commerce data to derive insights, and identify gaps and new opportunities

·      Develop scalable and effective machine-learning models and optimisation strategies to solve business problems across advertising, pricing and listings

·      Define and lead science initiatives from problem framing through production deployment in a high-ambiguity environment

·      Identify and build the sequential feedback loops that make decisions improve over time

·      Design evaluation frameworks to measure the quality and business impact at scale

·      Work closely with engineering, analytics and product teams to take models from experimentation into production

What We're Looking For

·      4+ years in Applied ML/AI, Data Science. Masters or PhD in a quantitative field is a plus

·      Deep proficiency in Python, SQL, statistics and data analysis

·      Hands-on experience developing, deploying and maintaining the end-to-end lifecycle of machine-learning models

·      Experience with LLMs, fine-tuning, AI agents, optimisation or sequential decision systems is a strong plus

·      Exposure to ecommerce, marketplaces, advertising or pricing data is valuable but not essential

·      Strong problem-solving and communication skills; ML research experience is a plus

WHY KILY

Kily is already working with leading brands including ITC, Unilever, Mondelez, Coca-Cola and Lenovo, and has recently raised an INR 30 crore ($3.1 mn) Seed round led by Sorin Investments, with participation from Razorpay and Wyser Capital. You'll have the opportunity to build a foundational AI system from an early stage - one designed not merely to generate insights, but to autonomously drive real-world business

outcomes.

Software Engineer

Company Summary :

As the recognized global standard for project-based businesses, Deltek delivers software and information solutions to help organizations achieve their purpose. Our market leadership stems from the work of our diverse employees who are united by a passion for learning, growing and making a difference. At Deltek, we take immense pride in creating a balanced, values-driven environment, where every employee feels included and empowered to do their best work. Our employees put our core values into action daily, creating a one-of-a-kind culture that has been recognized globally. Thanks to our incredible team, Deltek has been named one of America's Best Midsize Employers by Forbes, a Best Place to Work by Glassdoor, a Top Workplace by The Washington Post and a Best Place to Work in Asia by World HRD Congress. www.deltek.com

Business Summary :

The Deltek Engineering and Technology team builds best-in-class solutions to delight customers and meet their business needs. We are laser-focused on software design, development, innovation and quality. Our team of experts has the talent, skills and values to deliver products and services that are easy to use, reliable, sustainable and competitive. If you're looking for a safe environment where ideas are welcome, growth is supported and questions are encouraged – consider joining us as we explore the limitless opportunities of the software industry.

Position Responsibilities:

• Collaborate with the development team to maintain, enhance, and scale the product for enterprise use.
• Design and develop scalable, high-performance solutions using cloud technologies and containerization.
• Contribute to all phases of the development lifecycle, following SOLID principles and best practices.
• Write well-designed, testable, and efficient code with a strong emphasis on Test-Driven Development (TDD), ensuring comprehensive unit, integration, and performance testing.
• Ensure software designs comply with specifications and security best practices.
• Contribute ideas to improve application maintainability and performance under the guidance of senior engineers.

AI-Specific Responsibilities:

• Integrate AI-powered tools and frameworks to enhance code quality and development efficiency.
• Utilize AI-driven analytics to identify performance bottlenecks and optimize system performance.
• Implement AI-based security measures to proactively detect and mitigate potential threats.
• Leverage AI for automated testing and continuous integration/continuous deployment (CI/CD) processes.
• Support the adoption and effective use of AI agents for automating repetitive development, deployment, and testing processes within the engineering team.

Qualifications :

• 2-3 years of software engineering experience, including at least 1 year of hands-on AI/ML experience.
• Proficient in Node.js and TypeScript/React development, with experience in React 18+, Hooks (useState, useEffect, useContext), and WebSocket clients.
• Strong full-stack development experience with modern web technologies.
• Experience with ASP.NET Core (C#) is good to have.
• Experience developing REST APIs.
• Proficiency in front-end technologies (JavaScript, HTML, CSS, Bootstrap, and UI frameworks).
• Strong knowledge of cloud platforms (AWS preferred), including scalability and enterprise cloud. 
• Experience with security best practices in web and API development.
• Experience with Test-Driven Development (TDD).
• Strong database experience, focused on query optimization 
• Strong analytical skills, problem-solving abilities, and curiosity to explore new technologies.
• Ability to communicate effectively, including explaining technical concepts to non-technical stakeholders.
• High commitment to continuous learning, innovation, and improvement.

AI-Specific Qualifications:

• Practical experience with prompt engineering and LLM response optimization
• Knowledge of AI-driven development tools and platforms such as Claude Code, GitHub Copilot in Agentic Mode.
• Knowledge of AI-based security protocols and threat detection systems.
• Experience integrating GenAI or Agentic AI agents into full-stack workflows (e.g., using AI for code reviews, automated bug fixes, or system monitoring).
• Demonstrated proficiency with AI-assisted development tools and prompt engineering for code generation, testing, or documentation.

Location: Pune / Gurgaon

Position: AI Engineer

work mode: WFO

  Job Description.

​

 Job responsibilities:

• Responsibility for design, implementation and deployment of Generative AI, Agentic frameworks at scale
• Strong in programming - Python a
• Previous experience of working on Computer Vision projects and VLM /VLAM models.
• In depth awareness of Transformer architectures and End to End Deep neural networks
• Full stack AI / ML development experience
• Design, build & maintain efficient and reliable Agentic / Generative AI code leveraging pipelines
• Hosting and deployment knowledge in GCP or AWS or Azure along with advanced engineering concepts to build user friendly UI interface for easy adoption.

    Requirements:

 ·      4 to 8 years overall years of experience (Agentic AI, Generative AI, VLM, VLAM and LLM) with significant exposure in Development, Architecture design, scaling and hosting in cloud.

    Must Have –

 ·      Architecting and solutioning experience with Python and FAST API, Agentic Ai frameworks, VLMs, VLAMs, Open source LLM’s and Code based LLM models at scale with - Langchain /      Ollama, embeddings, Memory      Management etc.,

·      Practical experience in implementing Explainable and ethical AI models  Practical experience in implementing frameworks like RAG/ CAG/ Self-reflective RAG etc.,

·      Experience in cloud hosting either AWS or Azure or GCP.

·      Experience in ML-OPS - Implement a feedback mechanism to continually improve the model over time through feedback loop and monitoring KPI’s in production.

·      Experience with Quantization and Kubernetes or docker

    Good to have

·      gRPC implementation to expose the API’s on a server for easy usage and good user interface

·      Streamlit front end creation

·      Experience with SAFe framework deliveries.

Run delivery for 15–25 backend, frontend, and AI engineers building construction- tech and CAD drawing-automation software — with structural engineers on one side of you and the codebase on the other. Must have Hands-on familiarity with AutoCAD, Revit or experience at a product company in the CAD / AutoCAD / BIM / PLM / EDA /simulation space

Role overview-

We are hiring one Technical Product Manager to own delivery across our engineering teams in Bengaluru. You will run 15–25 engineers across backend, frontend, and AI workstreams, and you will be the person who turns requirements originating with our structural and detailing engineers into scoped, sequenced, shipped software.

This is a technical role. You will not be handed a groomed backlog and asked to move cards across it. You will sit in the design discussion and have a view on how and when — you will push back on an approach that will not scale, ask why a trade-off was made, and know the

difference between a two-day change and a two-sprint one. You are not expected to write production code. You are expected to read it, understand the system, and never be the least- informed person in a technical conversation you are chairing

What you’ll own- Delivery ownership

• Own the end-to-end delivery plan for every active workstream — scope, sequence,dependencies, capacity, and dates that actually hold.
• Break large, ambiguous product intent into engineering-sized increments with explicit acceptance criteria, agreed with the people who will build them.
• Run the operating cadence: planning, standups, refinement, demos, and retrospectives that change something the following week.
• Maintain one honest view of status — on track, at risk, slipped, and why — visible to the founders and to the team at the same time.
• Manage cross-team dependencies between backend, frontend, AI, and drawing-automation workstreams so no team idles waiting on another.
• Surface risk early, quantify it, and arrive with options — never an escalation without a proposal.
• Protect the team from thrash: absorb changing priorities, re-plan properly, and say no or not now when a plan cannot take more.
• Drive releases to genuinely done — QA, UAT with domain experts, documentation, and post- release verification included.
• Own and improve the delivery metrics that matter: cycle time, predictability of committed scope, defect escape rate, and rework.
• Run the release calendar across two products so neither becomes the permanent second priority.

The floor & the team.

Our requirements do not come from a product spec written in a vacuum. They come from structural and detailing engineers sitting on the floor with decades of practice behind them. Converting that into software is the defining skill of this job.

• Partner with structural and detailing engineers to turn drawing standards, project practice, and domain expectation into unambiguous, implementable requirements
• Run requirement sessions whose output is a written spec — sample data, expected output,edge cases, and explicit non-goals — not a shared verbal understanding.
• Arbitrate the constant gap between “how it has always been done on site” and “what can be deterministically automated.”
• Set up validation loops so domain experts review generated output early and repeatedly, rather than at the end of a sprint.
• Maintain a decision log for domain rules so the same question is not re-litigated three sprints later by different people.
• Manage internal stakeholder expectations and, where relevant, commitments made to customers.
• Spot when a “small clarification” is actually a scope change, and handle it as one.
• Make sure engineers get access to the domain expert directly — your job is to structure that contact, not to become a relay in the middle of it.

Challenges you’ll solve.

We prefer to be candid. These are the problems that make this role genuinely difficult — and genuinely interesting.

Domain knowledge that arrives as tribal knowledge

Our structural and detailing engineers know what a correct drawing looks like, but much of that knowledge is tacit. Your job is to extract it into deterministic, testable rules before an engineer starts building. Getting this wrong is the single most expensive failure mode we have

— it produces work that looks finished and is not.

Three disciplines, one release

Backend (AWS serverless), frontend (Angular / React), and AI engineers ship into the same

product. They have different failure modes, different testing regimes, and different natural cadences. Sequencing them so nobody idles and nothing integrates late is the core scheduling problem here.

Correctness is not negotiable

This is construction software. A wrong drawing, a wrong quantity, a wrong permission, or a wrong payroll figure has consequences outside the screen. “Ship it and iterate” has limits here, and you will need judgement about exactly where those limits sit for each workstream.

AI workstreams do not estimate like CRUD

Drawing generation and extraction work is research-shaped: some weeks produce a breakthrough, some produce a negative result. You will plan around that uncertainty honestly— with timeboxes, decision points, and fallbacks — instead of pretending an unknown is a two-

week ticket.

Live customers, finite engineers

Companies run their operations on this platform daily. Production incidents, customer escalations, and enterprise integrations (Asite, Autodesk Construction Cloud) compete with roadmap work for exactly the same people. You will make that trade-off explicitly, every week,

and be able to defend it.

Two products, one organisation

The operations platform and the drawing-automation platform have different customers, different rhythms, and partly shared people. Keeping both moving — without either becoming the perpetual second priority — is a standing constraint on every plan you make.

Founder-adjacent, fast-changing priorities

Direction can change on new customer information, and sometimes it should. You are the shock absorber: re-plan quickly, communicate the change clearly and once, and make sure the team experiences it as a decision rather than as chaos.

Qualifications.

• 6–7 years minimum in software delivery, including at least 4 years directly managing engineering teams as a project, delivery, or engineering program manager.
• Proven experience running teams of 10+ engineers across more than one discipline —backend, frontend, mobile, data, or AI.
• Fluency in modern delivery practice — Agile / Scrum or Kanban applied with judgement rather than ceremony — plus genuine facility with Jira or Linear, Git workflows, CI/CD, and release management.

Strongly preferred

• Experience at a product company in the CAD / AutoCAD / BIM / PLM / EDA /simulation space — Autodesk, Bentley, Dassault, Siemens, PTC, Trimble, Hexagon, Ansys or similar.
• Exposure to AEC, construction tech, manufacturing, or industrial software where domain correctness matters more than interface polish.
• Experience managing teams that included AI / ML engineers alongside conventional product engineers.
• Hands-on familiarity with AutoCAD, Revit, Civil 3D, Tekla, or DXF / drawing-export workflows.

The bar, the process, the offer.

The manager we’re looking for

Technical enough to be trusted

Engineers respect the questions you ask, not just the dates you set.

Owns outcomes, not activity

Measures the job by what shipped and worked, not by how busy the board looked.

Direct and specific

Says the uncomfortable thing early, in plain language, to the person who needs to hear it.

Reduces chaos

Leaves every process, plan, and handoff simpler than they found it.

Holds opinions loosely

Strong views on how to run delivery, updated when the evidence changes.

Raises the standard around them

The team becomes more predictable and more capable because you are in it.

What you'll do

The Software Engineer will serve as a full stack developer within the AI and Automation team, focusing on building AI agents, chatbots, and automation solutions. The role involves taking ownership and accountability to meet commitments, developing software using programming languages, and designing, executing, and reporting on system and service tests to ensure applications function as required. Additional responsibilities include monitoring, diagnosing, and resolving technology issues, as well as supporting team members by carrying out prescribed design activities using established procedures.

What you'll bring

3+ years of experience in full stack application development using Python (for backend) and JavaScript/Typescript (for frontend).

Strong understanding of Java.

Strong understanding of Machine Learning /MLOps using Python .

Familiarity with Agentic AI frameworks.

Familiarity with Kubernetes (K8S) and Docker.

Experience in developing solutions using Azure storage and compute solutions.

Proven experience working with Azure AI solutions, especially AI Foundry and AI Search.

Experience in developing distributed event-driven applications using brokers such as Apache Kafka, IBM MQ, Solace, or Azure Event Hub.

Proven experience in working with DevOps frameworks, GIT, and CI/CD pipelines.

Experience developing integrations using established Enterprise Application Integration patterns and tools such as Apache Camel.

Required Qualifications

Specify the minimum educational qualifications necessary for this role, including degree type, field of study, and level attained.

Applicants should hold a bachelor’s degree in any discipline and possess practical experience in software development. Degrees in Computer Science, Software Engineering, or Information Technology are particularly advantageous. 

We’re reimagining how users interact with SaaS products and looking for a founding engineer to help build the first version from zero alongside a founder with experience building at HubSpot, Asana, and Datadog.

Role

We are hiring a Founding Engineer with strong frontend, product, and design instincts. You will work directly with the founder to shape the product, prototype quickly, make architecture decisions, think through user problems, and turn an early thesis into a working product.

The ideal person is a builder — someone who can move from an ambiguous idea to prototype, from prototype to usable product, and from usable product to scalable architecture. You should be excited by the opportunity to change how users interact with software and, eventually, the internet itself.

What You’ll Build

You will help build the first version of our core product, including:

• An SDK powered by frontier AI models and modern product experience protocols
• Backend services with rules, resolution logic and intent inference
• Early prototypes that help us test different wedges with customers
• Production-ready systems as the product direction becomes clearer

This role will likely span React, TypeScript, Next.js, backend APIs, databases, event tracking, and AI-assisted workflows.

Required Experience

• 2–5 years of software engineering experience, or equivalent demonstrated ability
• Strong frontend engineering experience with React and TypeScript
• Ability to design clean, scalable frontend component architecture
• Experience with backend APIs and databases
• Experience taking ambiguous product ideas and turning them into working software

Strong Plus

• Experience building 0-to-1 products
• Experience at an early-stage startup
• Experience building SaaS products

What Success Looks Like

In the first 30 days, you will:

• Understand our thesis, target customer, and product direction
• Help refine the first product wedge
• Build and iterate on early prototypes
• Set up core frontend and backend foundations
• Move quickly across product, design, and engineering decisions

In the first 60–90 days, you will:

• Ship the first usable product prototype
• Help build demo experiences that clearly communicate the product vision
• Create the first version of the adaptive frontend architecture
• Help test the product with early users, customers, or design partners
• Identify what should become production-grade versus what should remain experimental

Leadership Opportunity

This is a founding role with significant upside.

For the right person, strong performance can lead to a leadership position as the company grows. We are looking for someone who can become a long-term technical and product partner — someone who can help build the first version, shape the architecture, hire future engineers, and potentially lead major parts of the engineering organization.

This is an opportunity to join at the earliest stage and help define both the product and the company.

Why Join

• Build from zero with high ownership
• Work directly with the founder
• Help create a new category of software
• Own meaningful technical decisions from day one
• Opportunity to join the leadership team as the company scales

Key Responsibilities

1. Solutioning & Proposal Development

•   Partner with Senior SMEs and Practice Leads to design end-to-end Data & AI solutions for client pursuits — contributing to structure, content, and commercial framing.

•   Build client proposals, solution documents, and program structures that are well-organized, accurate, and ready to use without significant rework.

•   Translate client requirements into structured, outcome-oriented learning journeys — adoption, capability uplift, and measurable business outcomes, not just module lists.

•   Support customized offerings across Data Engineering, AI / ML, and GenAI and Agentic AI tracks; help assemble pursuits from existing accelerators rather than rebuilding from scratch.

2. Client Engagement Support

•   Participate in client discussions, discovery calls, and requirement-gathering sessions — capture context with the rigour that makes the next conversation sharper.

•   Convert business needs into solution frameworks and delivery models with guidance from Senior SMEs; document customer priorities so Practice and Sales can act on them.

•   Support pitch decks, case studies, and success stories — buyer-specific, visually clean, and aligned to how the customer thinks about their own problem.

•   Stay engaged through the proposal cycle and handoff to delivery; ensure no requirement gets lost between discovery and execution.

3. Content & Program Structuring

•   Assist in designing curriculum outlines, learning journeys, and hands-on lab structures that hold up against real-world enterprise contexts.

•   Work with internal and external SMEs to ensure content aligns with current industry trends, real use cases, and business outcomes — not generic technology overviews.

•   Maintain a library of reusable program structures, slide assets, and case study inserts; flag gaps in the existing content library proactively.

4. Research & Market Intelligence

•   Track trends across the AI / GenAI / LLM ecosystem and Data Engineering & Analytics — translate findings into usable inputs for outreach, pitching, and offering design.

•   Identify new solution opportunities and product ideas based on market signals, customer asks, and competitor moves.

•   Benchmark StackRoute's offerings against competitors; surface gaps and differentiation angles for Senior SMEs and Practice Leads to act on.

5. Internal Collaboration

•   Work fluidly with Delivery, Sales, and external SMEs to ensure solutions designed on paper actually work in delivery — surface feasibility risks early, not late.

•   Coordinate inputs across Practice teams during pursuit cycles; hand off to delivery with documentation that captures customer commitments and success metrics.

Must Have Technical & Functional Skills

•   Good understanding of terminologies in Data Engineering (ETL, pipelines, data lakes), Data Analytics & BI concepts, and Machine Learning fundamentals, AI tools ( not technical expertise but L1-L2 knowledge should be present from application standpoint).

•   Awareness of GenAI / LLM vocabulary — prompt engineering, RAG, APIs — with enough depth to hold a credible first conversation with a technical stakeholder.

•   Strong PowerPoint skills (client-ready decks), Excel for effort estimation and costing basics, and structured documentation — proposals, SoWs, one-pagers.

•   Strong problem-solving and structured thinking — breaks complex requirements into clear, communicable solutions.

•   Comfortable communicating with both technical and non-technical stakeholders; good storytelling and presentation instincts; understanding of L&D context is a plus.

Qualifications & Experience

Required:

•   5-15 years in the education products, or in solutioning, pre-sales, or consulting roles with exposure of 3-5 years in Data/AI/Analytics.

•   Bachelor's or Master's in Computer Science, Data Science, Engineering, or a related discipline.

Nice to Have competences:

•   Prior pre-sales, proposal writing, or design development experience.

•   Certifications in cloud, data, or AI platforms (AWS, Azure, GCP, or model-provider certifications).

Core Competencies

·      Structured Thinking: Organises ambiguous client and technical inputs into logical, buyer-relevant narratives. Builds proposals that flow from problem to solution.

·      Solution Articulation: Translates Data & AI capabilities into crisp, persona-specific stories. Adapts the pitch for a CTO, L&D Head, or BU Head without losing substance.

·      Research & Synthesis: Gathers and distils large volumes of information into sharp, usable outputs. Knows what to include and what to leave out.

·      Written Communication: Writes a tight brief, a clean slide, and a clear email. Adapts register from internal working notes to buyer-facing collateral.

·      Curiosity & Learning Agility: Picks up new tools, concepts, and sectors quickly. Tracks AI / GenAI shifts proactively rather than waiting to be told what to read.

·      Bias for Action: Ships a useful 10-slide deck on time rather than a polished 20-slide deck that's late. Comfortable with iteration over perfection.

Key Responsibilities  

• Design, build, and optimize scalable data pipelines for AI/ML applications.
• Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
• Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
• Fine-tune open-source and foundation models using domain-specific datasets.
• Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
• Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
• Develop APIs and AI services for production deployment.
• Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
• Monitor model performance, troubleshoot production issues, and maintain technical documentation.

Required Skills  

Mandatory  

• 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
• Strong programming skills in Python and SQL.
• Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
• Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
• Experience in LLM fine-tuning and working with Hugging Face models.
• Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
• Experience with Git, REST APIs, Linux environments, and data processing libraries.

Preferred  

• Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
• Familiarity with Docker, Kubernetes, and MLflow.
• Exposure to Apache Spark or Airflow for data engineering workflows.
• Experience with cloud platforms (AWS, Azure, or GCP).

Primary Technology Stack  

• Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
• AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
• Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
• Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
• Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
• Vector Databases: Pinecone, Chroma, Milvus, Weaviate
• Databases: PostgreSQL, MongoDB
• MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
• Cloud Platforms: AWS, Azure, GCP

Experience: 1–3 Years

Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps

AI/ML Engineer (Fresher)

Location: Dewas / Indore (Work from Office)

Experience: Fresher (B.Tech 2024, 2025 & 2026)

Job Responsibilities

• Develop and implement AI/ML models.
• Work with Python, Machine Learning, and Generative AI.
• Build, test, and optimize AI-powered applications.
• Collaborate with cross-functional teams to deliver innovative solutions.

Required Skills

• B.Tech in CSE, IT, AI/ML, Data Science, or a related field.
• Strong knowledge of Python and AI/ML fundamentals.
• Basic understanding of Machine Learning, Deep Learning, NLP, LLMs, or Generative AI.
• Good analytical and problem-solving skills.
• Excellent verbal and written English communication skills.

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