50+ Machine Learning (ML) Jobs in India

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

Job Title: AI Architecture Intern

Company: PGAGI Consultancy Pvt. Ltd.

Location: Remote

Employment Type: Internship

Position Overview

We're at the forefront of creating advanced AI systems, from fully autonomous agents that provide intelligent customer interaction to data analysis tools that offer insightful business solutions. We are seeking enthusiastic interns who are passionate about AI and ready to tackle real-world problems using the latest technologies.

Duration: 6 months

Key Responsibilities:

• AI System Architecture Design: Collaborate with the technical team to design robust, scalable, and high-performance AI system architectures aligned with client requirements.
• Client-Focused Solutions: Analyze and interpret client needs to ensure architectural solutions meet expectations while introducing innovation and efficiency.
• Methodology Development: Assist in the formulation and implementation of best practices, methodologies, and frameworks for sustainable AI system development.
• Technology Stack Selection: Support the evaluation and selection of appropriate tools, technologies, and frameworks tailored to project objectives and future scalability.
• Team Collaboration & Learning: Work alongside experienced AI professionals, contributing to projects while enhancing your knowledge through hands-on involvement.

Requirements:

• Strong understanding of AI concepts, machine learning algorithms, and data structures.
• Familiarity with AI development frameworks (e.g., TensorFlow, PyTorch, Keras).
• Proficiency in programming languages such as Python, Java, or C++.
• Demonstrated interest in system architecture, design thinking, and scalable solutions.
• Up-to-date knowledge of AI trends, tools, and technologies.
• Ability to work independently and collaboratively in a remote team environment

Perks:

- Hands-on experience with real AI projects.

- Mentoring from industry experts.

- A collaborative, innovative and flexible work environment

Compensation:

- Stipend: Base is INR 8000/- & can increase up to 20000/- depending upon performance matrix.

After completion of the internship period, there is a chance to get a full-time opportunity as an AI/ML engineer.

Preferred Experience:

• Prior experience in roles such as AI Solution Architect, ML Architect, Data Science Architect, or AI/ML intern.
• Exposure to AI-driven startups or fast-paced technology environments.
• Proven ability to operate in dynamic roles requiring agility, adaptability, and initiative.

Job Title: Site Reliability Engineer (SRE) – AI & Cloud Infrastructure

Location: Pune (Work From Office)

Experience: 5–8 Years

Employment Type: Full-Time

About the Role

We are looking for an experienced Site Reliability Engineer (SRE) to build and scale AI-powered reliability capabilities from the ground up. In this role, you will drive modern observability, automation, and cloud reliability initiatives while leveraging AI/ML for incident management, forecasting, and infrastructure optimization.

You will own the end-to-end reliability strategy across cloud-native AWS environments, enabling high availability, performance, and operational excellence through automation, intelligent monitoring, and proactive engineering.

Key Responsibilities

• Design, implement, and manage highly available, scalable, and secure cloud infrastructure on AWS.
• Build and maintain an end-to-end observability platform using Open Telemetry, Grafana, Datadog, CloudWatch, and related tools.
• Implement AIOps capabilities, including:
• LLM-assisted incident triage
• AI-powered root cause analysis
• ML-driven forecasting and anomaly detection
• Intelligent alert correlation and noise reduction
• Lead production incident management, on-call response, postmortems, and Root Cause Analysis (RCA).
• Automate operational workflows using Infrastructure as Code (Terraform/CloudFormation) and CI/CD pipelines.
• Drive infrastructure rightsizing, capacity planning, utilization analysis, and cloud cost optimization.
• Build dashboards, SLOs, SLIs, and error budgets to improve service reliability.
• Develop automation scripts using Python, Bash, or Go to eliminate manual operational tasks.
• Monitor application and infrastructure health while ensuring high uptime and service performance.
• Collaborate with Development, DevOps, Security, Platform Engineering, and Product teams across multiple time zones.
• Establish operational best practices for monitoring, incident response, disaster recovery, and resilience engineering.
• Maintain Linux-based production systems and troubleshoot OS, networking, storage, and performance issues.

Required Skills & Qualifications

• 5–8 years of experience in Site Reliability Engineering, DevOps, or Cloud Infrastructure Engineering.
• Strong experience with AWS services including EC2, ECS/EKS, Lambda, VPC, IAM, CloudWatch, RDS, Route 53, S3, and Auto Scaling.
• Hands-on experience with Open Telemetry, Grafana, Datadog, Prometheus, or similar monitoring platforms.
• Strong knowledge of Linux administration, networking, system performance tuning, and troubleshooting.
• Experience with Infrastructure as Code using Terraform or CloudFormation.
• Proficiency in scripting using Python, Bash, or Go.
• Experience with Kubernetes and containerized workloads.
• Strong understanding of CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI, etc.).
• Experience leading incident management, production support, and RCA processes.
• Knowledge of SRE principles including SLIs, SLOs, and Error Budgets.
• Experience implementing monitoring, logging, alerting, and observability frameworks.
• Strong analytical, troubleshooting, and communication skills.

Preferred Qualifications

• Experience building or implementing AIOps solutions.
• Exposure to Large Language Models (LLMs) for operational automation.
• Experience with machine learning-based forecasting or anomaly detection.
• Hands-on experience administering Adobe Experience Manager (AEM).
• Experience managing Cloudflare CDN, WAF, DNS, and caching strategies.
• Knowledge of FinOps, cloud cost optimization, and capacity planning.
• AWS Solutions Architect, DevOps Engineer, or Kubernetes certifications are a plus.

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.

AI/ML Engineer – Fresher

Location: Remote

Experience: 0–1 Year

About the Role

We are looking for a Fresher/AI-ML Intern who has hands-on experience through internships, academic projects, hackathons, or personal projects and is interested in building AI solutions for the Higher Education and SaaS domain.

Required Skills

• Strong knowledge of Python and basic ML/AI concepts
• Hands-on experience with LLMs, RAG, AI Agents, Prompt Engineering
• Knowledge of LangChain / LangGraph / LlamaIndex
• Exposure to PyTorch / TensorFlow / Hugging Face
• Basic experience with FastAPI / REST APIs
• Knowledge of Vector Databases – Pinecone, Qdrant, ChromaDB, FAISS, or pgvector
• Basic understanding of Git, Docker, and AWS
• Good understanding of Computer Vision / NLP is a plus

Preferred Project Experience

Candidates with projects/internships in any of the following will be preferred:

• RAG / LLM / AI Agents
• Computer Vision / CCTV / Video Analytics
• Face Recognition
• OCR / OMR
• Trading / Financial AI
• NLP / Document Intelligence
• AI Automation / EdTech / SaaS

Who Can Apply?

• Freshers or candidates with 0–1 year experience
• Candidates with relevant AI/ML internship experience
• Must have at least 1–2 hands-on AI/ML projects and be able to explain the implementation clearly.

If you are passionate about AI/ML and want to work on real-world AI applications, we would love to hear from you.

We are seeking an innovative and experienced Machine Learning Engineer at Architect level with a strong foundation in both traditional data science and modern Generative AI. The ideal candidate will lead the design, development, and deployment of high-impact, data-driven solutions on our Azure cloud infrastructure. You will be responsible for architecting complex systems, including multi-agent platforms and computer vision solutions, optimizing legacy models, and providing technical leadership to cross-functional teams to solve challenging business problems.

Must-Have Skills & Experience:

• Proven experience architecting, developing, and deploying traditional and deep learning solutions at scale, from concept to production.
• Lead end-to-end ML lifecycle including data preparation, feature engineering, model development, validation, deployment, and monitoring.
• Provide technical leadership, mentorship, and architecture-level guidance to project teams.
• Demonstrated expertise in designing and implementing complex multi-agent systems.
• Experience with agentic design patterns such as supervisor-worker and orchestrator-led group chats to automate intricate business processes (e.g., invoice processing, document automation).
• Experience with data augmentation techniques and human-in-the-loop annotation processes for large-scale model training.
• Evaluate and optimize existing models using traditional ML techniques. Proven expertise in traditional ML algorithms (regression, decision trees, SVM, ensemble models, clustering, Random Forest, XGBoost).
• Deep understanding of ML pipeline orchestration and model lifecycle management with production-grade implementation experience.
• Ensure adherence to MLOps best practices and drive implementation on Azure cloud.
• Extensive experience in Azure cloud services including Azure Machine Learning, Azure Data Factory, Blob Storage, Azure DevOps, and Azure Container Apps.
• Leveraged Azure Cognitive Search and Azure OpenAI Service to build scalable and efficient knowledge retrieval systems, enabling real-time semantic search and contextual answer generation.
• Designed and implemented RAG pipelines on Microsoft Azure, integrating large language models (LLMs) with domain-specific knowledge bases to enhance AI-driven information retrieval and response accuracy.
• Experience with evaluation, monitoring and observability frameworks for Agentic workflows.
• Experience designing fault-tolerant systems with robust error handling, fallback mechanisms, and state management for complex, multi-step AI workflows.
• Ability to design and review ML architecture and system integration strategies with hands-on experience in production deployments.
• Certifications in Azure AI Engineer or Azure Solutions Architect.
• Excellent problem-solving, communication, and stakeholder management skills with experience presenting technical solutions to business stakeholders.

Good-to-Have Skills:

• Collaboration skills with data scientists, data engineers, and product stakeholders to convert business requirements into scalable ML models.
• Contributions to open-source projects.

We are seeking an experienced MLOps Architect who can drive end-to-end implementation of the proposal being prepared for the initiative and also contribute broadly across other enterprise AI/ML programs. This role demands a strong architectural mindset, hands-on technical depth, and the ability to design scalable, cloud-native machine learning operations across traditional ML and modern LLM workflows.

The ideal candidate will bring experience with SageMaker-based MLOps pipelines, evaluation of equivalent tooling stacks, hybrid MLOps/LLMOps automation, CI/CD orchestration, governance, and production-grade scalability patterns.

Must have skills & Qualifications:

• 8+ years working in ML/AI engineering or MLOps roles with strong architecture exposure.
• Strong expertise in AWS cloud-native ML stack, including: SageMaker(primary), ECS, Lambda, API Gateway, CI/CD (CodeBuild/CodePipeline or equivalent)
• Hands-on experience with at least one major MLOps toolset and awareness of alternatives: MLflow, Kubeflow, SageMaker Pipelines, Airflow, BentoML, KServe, Seldon.
• Deep understanding of model lifecycle management (feature engineering->training -> registry -> deployment -> monitoring).
• Experience implementing or supporting LLMOps pipelines, including: prompt versioning, evaluation metrics, automation frameworks.
• Deep understanding of ML lifecycle: data ingestion, feature engineering, training, evaluation, model packaging, CI/CD, drift detection, monitoring, and governance.
• Strong experience with AWS SageMaker (Pipelines, Feature Store, Model Registry, Model Monitor).
• Experience implementing ML CI/CD pipelines including automated training, testing, validation, model promotion, and endpoint deployment.
• Strong SQL and data transformation experience using Snowflake, Databricks, Spark.
• Experience with feature engineering pipelines and Feature Store management.
• Understanding of lineage tracking: training data snapshot, feature versions, code versioning, metadata tracking, reproducibility.
• Hands-on experience with Bedrock, OpenAI, Anthropic, or Llama models.
• Experience with CloudWatch, SageMaker Model Monitor, Prometheus/Grafana.
• Strong foundation in Python and cloud-native development patterns.
• Solid understanding of security best practices, IAM, secrets management, and artifact governance.

Good to have skills:

• Experience with vector databases, RAG pipelines, or multi-agent AI systems.
• Exposure to DevOps and infrastructure-as-code (Terraform, Helm, CDK).
• Hands-on understanding of model drift detection, A/B testing, canary rollouts, and blue-green deployments.
• Familiarity with Observability stacks (Prometheus, Grafana, CloudWatch, OpenTelemetry).
• Knowledge of Lakehouse (Delta/Iceberg/Hudi) architecture.
• Ability to translate business goals into scalable AI/ML platform designs.
• Strong communication and cross-team collaboration skills.
• Ability to guide engineering teams through technical uncertainty and design choices.

Key Responsibilities:

• Architect and implement the MLOps strategy for the programme, ensuring alignment with the project proposal and delivery roadmap.
• Design and own enterprise-grade ML/LLM pipelines covering model training, validation, deployment, versioning, monitoring, and CI/CD automation.
• Build container-oriented ML platforms (EKS-first) while evaluating alternative orchestration tools with similar capabilities (Kubeflow, SageMaker, MLflow, Airflow, etc.).
• Implement hybrid MLOps + LLMOps workflows, including prompt/version governance, evaluation frameworks, and monitoring for LLM-based systems.
• Serve as a technical authority across multiple internal and customer projects, contributing architectural patterns, best practices, and reusable frameworks.
• Enable observability, monitoring, drift detection, lineage tracking, and auditability across ML/LLM systems.
• Collaborate with cross-functional teams — data engineering, platform, DevOps, and client stakeholders — to deliver production-ready ML solutions.
• Ensure all solutions adhere to security, governance, and compliance expectations, particularly around handling cloud services, Kubernetes workloads, and MLOps tools.
• Conduct architecture reviews, troubleshoot complex ML system issues, and guide teams through implementation across cloud-native ML platforms.
• Mentor engineers and provide guidance on modern MLOps tools, platform capabilities, and best practices. 

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

Principal 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

Position Responsibilities :

About the Role 

We are seeking a highly motivated AI Solutions Engineer to join Deltek’s growing AI Center of Excellence team to design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate combines deep expertise in AI, machine learning, Generative AI, Large Language Models (LLMs), SLMs, software engineering, cloud computing, and MLOps/LLMOps to build scalable, production-grade AI applications. 

The AI Solutions Engineer will collaborate with AI data scientists, architects, and engineering teams to deliver innovative AI-driven solutions while ensuring security, scalability, governance, and operational excellence. This role reports to the Senior AI Solutions Architect. 

Key Responsibilities 

AI & Machine Learning Development 

• Design, build, train, evaluate, and deploy machine learning and deep learning models. 
• Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral. 
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks. 
• Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions. 
• Optimize model performance, scalability, latency, and cost. 

Software Engineering & Solution Development 

• Develop production-grade AI applications using Python and modern software engineering practices. 
• Build APIs, microservices, and AI-powered enterprise applications. 
• Integrate AI services with enterprise systems, business applications, and data platforms. 
• Apply coding standards, automated testing, CI/CD, and version control best practices. 

MLOps & AI Operations 

• Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management. 
• Automate model training, validation, testing, and deployment processes. 
• Monitor model performance, data drift, hallucinations, and operational metrics. 
• Support continuous improvement and reliability of AI platforms. 

Cloud & Platform Engineering 

• Develop AI solutions on Azure, AWS, or Google Cloud platforms. 
• Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies. 
• Build scalable architectures supporting enterprise AI workloads and real-time inference. 

AI Governance & Security 

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements. 
• Implement model governance, explainability, bias mitigation, and risk management practices. 
• Maintain standards for secure design, deployment, and operation of AI solutions. 

Required Qualifications 

Education 

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field. 

Experience 

• 5+ years of software engineering or machine learning development experience. 
• 2+ years of hands-on experience developing and deploying Agentic AI, Generative AI or AI/ML solutions in production environments. 

Technical Skills 

Programming & Engineering 

• Strong expertise in Python. 
• Experience with Java, ReactJS, JavaScript, or similar programming languages. 
• Solid understanding of algorithms, data structures, APIs, and software design principles. 

Artificial Intelligence & Machine Learning 

• Machine Learning and Deep Learning concepts and frameworks. 
• Model training, evaluation, optimization, and deployment. 

Generative AI 

• Large Language Models (LLMs) & SLMs 
• Prompt Engineering 
• Retrieval-Augmented Generation (RAG) 
• AI Agents and Agentic Workflows 
• Fine-tuning and model customization 
• Vector embeddings and semantic search 

Frameworks & Tools 

• PyTorch, TensorFlow, Scikit-learn 
• LangChain, LlamaIndex, Semantic Kernel, MCP, A2A and Transformers 
• FastAPI, Flask 

Data & Analytics 

• SQL and NoSQL databases 
• Data pipelines, ETL, and data modeling 
• Experience with AWS, Azure and Google 

MLOps & DevOps 

• MLflow, Kubeflow, Azure ML, SageMaker 
• Docker and Kubernetes 
• Git, GitHub, Azure DevOps, Jenkins 
• CI/CD automation and model monitoring 

Cloud Platforms 

• AWS (preferred) 
• AWS Bedrock or Azure OpenAI Service 
• AWS SageMaker 
• Google Vertex AI 

Preferred Qualifications 

• Experience designing enterprise-scale AI platforms and products.  
• Knowledge of multi-agent architectures and autonomous AI systems.  
• Experience with vector databases such as Pinecone, Snowflake Cortex, Pgvector, Weaviate, Chroma, or Azure AI Search.  
• Understanding of AI governance, compliance, and Responsible AI frameworks.  
• Relevant certifications in Azure AI, AWS Machine Learning, or Google Cloud AI.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

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

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

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

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

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

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

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

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

7

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10

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

11

Mandatory (Age) - Candidate's Age should be below 37 years.

12

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

13

Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

14

Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

15

Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

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

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

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

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

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

10

Mandatory (Age) - Candidate's Age should be below 30 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

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

AI Engineering Lead – Air Cargo Practice

Position Summary

Lead the company's Artificial Intelligence strategy, development, and deployment of AI powered solutions for air cargo revenue management, dynamic pricing, demand forecasting, and optimization. This role combines AI leadership, software engineering, data science, and airline cargo domain expertise to deliver innovative products that improve airline revenue, capacity utilization, and commercial decision-making.

Key Responsibilities -

• Define and execute the AI roadmap aligned with company strategy and product vision.
• Design and deploy AI, Machine Learning, Generative AI, and Agentic AI solutions.
• Build scalable AI platforms, MLOps processes, and governance frameworks.
• Collaborate with product, engineering, commercial, and customer teams.
• Lead and mentor AI engineers and data scientists.
• Support customer engagements, product demonstrations, and thought leadership.

Qualifications -

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Operations Research, Engineering, or related field. -
• 10+ years of experience in AI/ML, software engineering, or advanced analytics.
• 3+ years leading AI, machine learning, or data science teams.
• Expertise in Python, Machine Learning, Deep Learning, Generative and Agentic AI, LLMs, optimization, and cloud platforms
• Experience deploying production-grade AI solutions. - Knowledge of revenue management, pricing, forecasting, or optimization systems.
• Air cargo, airline, logistics, or transportation industry experience preferred. Success Measures
• Deliver AI-powered pricing and revenue optimization capabilities.
• Improve forecast accuracy and pricing effectiveness.
• Launch Agentic AI solutions that automate commercial decision-making.
• Establish scalable AI engineering practices and governance.
• Position the company as an industry leader in AI-driven air cargo revenue management.

Ideal Candidate

A hands-on AI leader who can bridge advanced technology and business value, capable of leading a group of Data Scientists, engaging with airline executives, and delivering innovative AI solutions that drive measurable revenue and profitability improvements.

Job Title: AI Developer

Location: Bangalore / Hyderabad

Experience: 5–7 Years

Notice Period: Immediate to 15 days Preferred

About the Role

We are looking for a skilled AI Developer to design, develop, and deploy Artificial Intelligence and Machine Learning solutions that solve real-world business problems. You will work closely with data scientists, engineers, and business stakeholders to build scalable AI-powered applications and pipelines.

Key Responsibilities

• Design and develop Machine Learning models, Deep Learning architectures, and NLP solutions for production use.
• Build and maintain end-to-end ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment.
• Integrate AI/ML models into existing applications and APIs.
• Fine-tune and evaluate Large Language Models (LLMs) for business-specific use cases.
• Collaborate with data engineers to ensure high-quality training data.
• Monitor model performance in production and implement retraining strategies.
• Research and evaluate emerging AI frameworks, tools, and technologies.
• Document models, experiments, and technical decisions.
• 
  

Required Skills & Experience

• 3+ years of experience in AI/ML development.
• Strong proficiency in Python.
• Hands-on experience with TensorFlow, PyTorch, or Scikit-learn.
• Experience with NLP frameworks such as Hugging Face, spaCy, and LangChain.
• Familiarity with LLMs and Prompt Engineering (OpenAI, Anthropic, Gemini, etc.).
• Experience deploying models using FastAPI or Flask (REST APIs).
• Strong understanding of Data Structures, Algorithms, and Statistics.
• Experience with AWS, Azure, or GCP and MLOps tools.
• Experience with Vector Databases such as Pinecone, Weaviate, or FAISS is an added advantage.

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

7

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10

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

11

Mandatory (Age) - Candidate's Age should be below 37 years.

*JD- Lead Developer*

 Seeking an experienced C++/Qt Tech Lead to lead the design and delivery of high-performance, cross-platform software solutions. In this role, you will own technical architecture, drive engineering best practices, and mentor the team while collaborating closely with product and stakeholder groups.

Title: Lead Developer

Location: Bengaluru

Qualifications: Bachelor’s or master's degree in computer engineering or computer science.

Experience 10+ years of experience.

Type: Full-time

Skills Required

Bachelor’s or master's degree in computer science, engineering, or a related field.

10+ years of professional experience in C++ development, including ownership of complex modules or systems.

* Deep expertise in the Qt framework (Qt Widgets) for building robust, cross-platform desktop applications.

 Strong understanding of OOP principles, design patterns, and system design; able to guide design reviews and trade-off decisions.

 Proven experience building multi-threaded, performance-critical applications with a focus on concurrency and stability.

 Strong knowledge of modern C++ and standard libraries; comfortable with writing clean, testable, and maintainable code.

 Hands-on expertise in debugging, profiling, and performance optimization using appropriate tools and techniques.

 Strong problem-solving skills with the ability to take ownership in ambiguous situations and deliver results.

 Excellent communication and leadership skills; able to align teams and stakeholders on technical direction.

Tasks & Activities

Proven, real-world experience architecting or leading healthcare applications, medical imaging, and graphics computing platforms.

 Good understanding of CI/CD concepts, build and release automation, advanced computing, and core data structures.

 Experience working in Agile/Scrum development environments. (must)

 Strong working knowledge of version control systems such as Git (SVN is a plus).

 Demonstrated experience designing systems for imaging datasets and other performance-critical workflows.

 Experience applying AI/ML to medical or dental image analysis.

 Exposure to assisted or automated diagnostic systems is a plus. Training staff / Onsite clients on software use.

 Incorporate new technologies into the products.

 Create technical and regulatory documents for the project.

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

7

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10

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

11

Mandatory (Age) - Candidate's Age should be below 37 years.

12

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

13

Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

14

Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

15

Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

Job Title: QA Engineer – Manual Testing

Experience: 3 – 5 Years

Location: Mohali (Phase 8B, Industrial Area)

Company: 75WAY Technologies Pvt. Ltd.

Key Responsibilities

• Design, develop, and execute comprehensive manual test cases, test scenarios, and test plans.
• Perform functional, regression, integration, system, smoke, and sanity testing.
• Test AI/ML applications, including chatbot workflows, LLM-based features, recommendation engines, and intelligent automation systems.
• Validate AI-generated responses for accuracy, relevance, consistency, and edge-case handling.
• Test REST APIs using tools like Postman or Swagger.
• Identify, document, track, and verify defects using bug-tracking tools.
• Collaborate with developers, AI engineers, business analysts, and product managers to understand requirements and validate implementations.
• Conduct cross-browser, cross-platform, and responsive testing for web and mobile applications.
• Verify application performance from a functional perspective and ensure seamless user experience.
• Participate in sprint planning, requirement reviews, and release validation.
• Prepare detailed test reports and maintain testing documentation.
• Suggest improvements to QA processes and testing methodologies for AI-driven applications.

Are you a Python-savvy developer with experience & portfolio of working on python, AI, ML projects looking to work on innovative projects? Look no further! SJTech Solutions is seeking an ambitious and talented candidate to join our dynamic team. 

Key responsibilities:

1. Develop and implement AI models using Python, Machine Learning, Data Science, and Deep Learning techniques.

2. Working on deep learning and machine learning algorithms

3. Working on automation scripts

4. Working on supervised and unsupervised learning algorithms

5. Stay up-to-date with the latest advancements in AI technology and trends.

If you are passionate about AI and eager to work and grow in a fast-paced environment, we want to hear from you! Apply now!

About Blitzy

Blitzy is a Cambridge, MA based AI software development platform on a mission to revolutionize the software development life cycle by autonomously building custom software to unlock the next industrial revolution. We're transforming how enterprises build software, turning enterprise requirements into production-ready code with an agentic software development platform that can autonomously execute 80% of the quantum of software development work. We're backed by multiple tier 1 investors, and have proven success as founders of previous start-ups.

Location: Pune, India (in-person role, HQ2)

The Role

We're looking for a Forward Deployed Engineer to join our Pune HQ2 team, thriving at the intersection of cutting-edge AI technology and real-world enterprise software challenges. You'll embed directly with customer engineering teams, helping them revolutionize their software development with Blitzy.

This isn't a typical solutions engineer role. You'll write production code, architect solutions, and work shoulder-to-shoulder with customer engineering teams to deliver transformative outcomes. You're equally comfortable debugging a complex integration, participating in sprint planning, or whiteboarding architecture with a CTO.

This role centers on our Java/Spring Boot enterprise stack — from core REST services and event-driven microservices to production operations — and requires close collaboration with our US-based engineering and customer teams, including time zone overlap for handoffs.

What Success Looks Like

• You deploy Blitzy into complex enterprise environments, navigating technical constraints while delivering immediate value
• You write custom integrations and extensions — built on Java 17 and Spring Boot 3.x — that unlock new use cases for customers
• You translate ambitious business goals into concrete technical implementations using Blitzy's AI capabilities
• You identify patterns across customer needs that influence our product roadmap
• You become a trusted technical advisor to engineering leaders, earning their confidence through hands-on expertise
• You drive successful POCs that expand into enterprise-wide deployments
• You contribute code and insights back to our core platform based on field experience

Areas of Ownership

Customer Partnership

• Embed with customer teams during critical implementations, serving as their technical co-pilot
• Participate in customer sprint planning, architecture reviews, and technical decision-making
• Build custom solutions that demonstrate Blitzy's capabilities in their specific environment

Technical Implementation

• Write production-quality Java 17 / Spring Boot 3.x services (Spring MVC, Spring Data JPA, Spring Security, Actuator) to integrate Blitzy with existing enterprise systems
• Build and evolve REST APIs using a contract-first approach with OpenAPI/Swagger
• Own data persistence with PostgreSQL, Hibernate/JPA, and Liquibase-managed database migrations
• Package and deploy containerized services with Docker, managing dependencies through Maven (BOM and multi-module projects)
• Develop reusable patterns, scripts, and tools that accelerate customer adoption
• Debug complex issues spanning customer infrastructure and our platform
• Create technical demos that showcase advanced use cases

Quality & Production Operations

• Write thorough test coverage using JUnit 5, Cucumber/Gherkin, and JMeter for performance validation
• Maintain code quality and observability with Git, Logback/SLF4J, SpotBugs, and SonarQube
• Support microservice architectures and production operations, including health checks, metrics, and configuration management

Strategic Impact

• Decompose executive vision into actionable engineering roadmaps
• Identify expansion opportunities within accounts based on technical insights
• Collaborate with our product team to prioritize features based on field experience
• Contribute to our technical documentation and best practices

Required Experience

• 4–10 years of software engineering experience with a track record of shipping production code
• Strong hands-on experience with Java 17 (must-have)
• Spring Boot 3.x expertise, including Spring MVC, Spring Data JPA, Spring Security, and Actuator
• PostgreSQL and Hibernate/JPA for data persistence
• Maven for dependency management (BOM and multi-module projects)
• Liquibase for database migrations
• REST API development using a contract-first/OpenAPI approach with Swagger
• Docker for containerized services
• Testing and performance tooling: JUnit 5, Cucumber/Gherkin, and JMeter
• Git, Logback/SLF4J, and static analysis tools such as SpotBugs and SonarQube
• Familiarity with microservice architectures and Spring Boot production operations (health checks, metrics, configuration)
• Deep understanding of modern software development practices (CI/CD, microservices, cloud architecture)
• Experience with enterprise development workflows, including Agile/Scrum methodologies
• Strong communication skills, with time zone overlap to support handoffs with our US-based teams—you can explain technical concepts to anyone from intern to CTO
• Must be based in or willing to work in-person from Pune, India

What Makes You Stand Out

• Jakarta EE experience, particularly Spring Boot 3 migrations
• Caching experience using Spring Cache
• Familiarity with Apache HttpClient 5
• Enterprise modernization experience: Java 8 → Java 17 upgrades and monolith → microservices migrations
• Exposure to event-driven architecture, including Spring Kafka, Apache Kafka consumers, dead-letter topics (DLT), and retry patterns
• Experience with Confluent Schema Registry and Apache Avro, including schema evolution and contract-first integrations
• Familiarity with Spring RestClient, Testcontainers, WireMock, and JaCoCo
• Observability experience with Micrometer and Dynatrace
• Exposure to Saga orchestration, resilience/retry patterns, Kubernetes, or the WSO2 API Gateway
• Experience at high-growth startups or in customer-facing engineering roles
• Background in AI/ML, developer tools, or enterprise modernization projects
• Track record of technical thought leadership (blogs, talks, open source contributions)
• Passion for the future of AI-powered software development

What Makes This Role Different

• Production Code, Real Stakes: Unlike traditional FDE roles that focus on configuration and support, you'll be writing production Java/Spring Boot code that directly impacts customer success and enterprise deals you can see the outcome of.
• Frontier AI Work: You'll shape AI adoption at the frontier of what's possible with generative AI—working with a platform that generates complete applications, not just code snippets.
• Influence the Roadmap: You'll influence product direction through direct field experience, feeding real customer insight back into our roadmap.
• Onsite Opportunities: High performers are eligible for company-sponsored trips to our Cambridge, MA headquarters based on business needs, with potential international transfer support for top talent.
• Full Employment Package: This is a full-time role directly with our Private Limited Company in Pune. Benefits include medical insurance, participation in the company stock option program, Employer Provident Fund contributions with gratuity after 5 years, technology allowance, meal provision, and a beautiful office environment.
• Clear Growth Path: Flexibility to transition into core product or platform engineering as Blitzy scales.

Our Culture

Who we are:

Led by two pioneering co-founders we are one of the fastest growing companies in the U.S., creating our own category of enterprise autonomous software development. We automate thousands of hours of software development for our customers, which includes strong representation within the Fortune 500.

How we work:

• We move Blitzy Fast: Time is both our company's and our clients' most precious asset. We move quickly and decisively to innovate internally and deliver exceptional software externally.
• Championship Mindset: We operate like a professional sports team. We win as a team by holding ourselves and each other to high standards, collaborating in-person, and remaining focused on the mission.
• Passion for Invention: We're pushing the frontier of what's possible, requiring constant innovation and iteration.
• We Work for the Customer: We focus on delivering outsized value to the customers we work with and expanding those relationships into deep, meaningful partnerships.

We believe in being 'everyday athletes'—taking care of ourselves so we can bring our best minds to work. We promote great sleep, movement, and restorative activities for optimal mental performance. It makes for a happier and more productive team.

Strong Lead Data Science, / AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

3

Mandatory (Experience 2) - Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

4

Mandatory (Experience 3) - Candidate's Current designation must be Lead or above.

5

Mandatory (Experience 4) - Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

6

Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

7

Mandatory (Experience 6) - Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

8

Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

9

Mandatory (Experience 8) – Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

10

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

11

Mandatory (Age) - Candidate's Age should be below 37 years.

12

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

13

Preferred (Experience 1) - Candidates currently working as Lead / Principal / Engineering Manager / Associate Director / Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers will be preferred.

14

Preferred (Experience 2) - Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India are encouraged to apply.

15

Preferred (Experience 4) - Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.

Location: Mumbai (Work From Office)

About Quantiphi

Quantiphi is an award-winning AI-first digital engineering company driven by the desire to solve transformational business problems. By combining deep industry expertise, cloud and data engineering, and cutting-edge AI research, Quantiphi helps customers build smarter products, frictionless customer experiences, autonomous processes, and safer businesses.

As an Architect – Machine Learning Engineer, you will design and develop advanced machine learning models and algorithms to solve complex business problems. You will optimize and deploy these models on AWS infrastructure, ensuring scalability and reliability.

Must Have Skills

• 7+ years of hands-on experience implementing and developing cloud ML solutions on AWS.
• Strong experience with AWS SageMaker, including:
• Training Jobs
• Processing Jobs
• Batch & Real-time Inference
• Working with multiple data sources
• Strong NLP expertise:
• Deep Learning concepts (Transformers, BERT, Attention Models)
• Python
• Hugging Face Transformers
• SpaCy
• NLTK
• Stanford NLP
• NLP concepts including tokenization, embeddings, syntactic & semantic parsing, Named Entity Recognition (NER), and coreference resolution.
• Experience building Agentic AI applications:
• LangChain
• Amazon Bedrock Agents
• Autonomous task planning and multi-step reasoning
• Experience architecting AI solutions using AWS services:
• AWS Lambda
• Amazon Bedrock
• Step Functions
• S3
• API Gateway
• SageMaker
• Experience implementing Model Context Protocol (MCP) for state, memory, context window, and prompt orchestration.
• Experience integrating agentic workflows with LLMs such as:
• Titan
• Nova
• Cohere
• Claude
• Hands-on experience fine-tuning Large Language Models (LLMs), specifically Llama 2.
• Familiarity with:
• Prompt Engineering
• Tool Calling
• Vector Databases (OpenSearch, Pinecone, Elasticsearch, Bedrock Knowledge Bases)
• Context Management
• Model evaluation and optimization:
• Zero-shot/Few-shot evaluation
• Hyperparameter tuning
• Model interpretability
• Experience with workflow orchestration tools such as:
• Airflow
• AWS Step Functions
• SageMaker Pipelines
• Kubeflow
• Experience building secure, scalable APIs and integrating third-party data sources.
• Strong collaboration skills with Developers, QA, Product Managers, and cross-functional stakeholders.

Good to Have

• Experience working on EdTech use cases.
• Software development experience.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications:

• Master’s degree or PhD in Computer Science or a related field with a focus on Artificial Intelligence (AI) or Machine Learning (ML).
• 7 years of software experience designing and deploying large-scale AI/ML applications and complex models.
• Experience with MLOps, establishing best practices for production model monitoring, maintenance, and lifecycle management.
• Experience leading and mentoring engineers, and translating complex business objectives into scalable AI-driven roadmaps.
• Experience architecting scalable data pipelines using BigQuery, Beam, or Dataflow, and deploying models on Google Cloud Platform.
• Understanding of Python and frameworks like TensorFlow, JAX, and Scikit-learn, with deep statistical modeling knowledge.

About the job

The People Operations Engineering team is dedicated to building innovative and scalable technology solutions that empower a global workforce. Leveraging the power of data, AI, and machine learning, the team optimizes HR processes, enhances employee experiences, and drives organizational efficiency. Joining this team offers the opportunity to lead the application of AI to solve complex, real-world business challenges.

We are seeking a highly experienced and results-oriented Senior AI Software Engineer to join the Human Resources Engineering (HRE) team in Hyderabad.

In this role, you will serve as a technical leader, driving the design, development, and deployment of sophisticated AI-powered solutions that significantly impact employees across the organization. You will work on large-scale infrastructure, mentor a talented team of engineers, collaborate with cross-functional stakeholders, and help shape the future of HR through innovative AI applications. You will be responsible for translating complex business challenges and strategic objectives into scalable, efficient AI/ML systems.

As part of the Corporate Engineering organization, you will build world-class business solutions that support a global enterprise. The team delivers end-to-end platforms, tools, and experiences that enable employees to work more effectively and drive innovation across the organization.

Responsibilities

• Lead the technical design and architecture of complex, scalable AI/ML systems and infrastructure within the HR engineering domain, ensuring high reliability, performance, and long-term sustainability.
• Drive the development of advanced AI/ML algorithms for HR applications such as talent acquisition and employee engagement using Python and frameworks like TensorFlow and JAX.
• Architect large-scale data pipelines for data ingestion, cleaning, and feature engineering using modern data processing frameworks (e.g., Beam, Dataflow), while leading root cause analysis and system troubleshooting.
• Own the end-to-end MLOps lifecycle, including model deployment, monitoring, optimization, and maintenance, while establishing best practices for software development and code quality.
• Collaborate with cross-functional stakeholders to translate business needs into scalable AI/ML solutions, mentor engineers, and drive the adoption of emerging AI technologies across the organization.

Python Machine Learning Engineer / Data Scientist (GCP)

Experience: Typically 6–10 Years (based on JD complexity)

Primary Skills: Python, Machine Learning, Data Analysis, Google Cloud Platform (GCP)

Must-Have Skills (ON)

• Python
• Machine Learning
• Data Analysis
• Exploratory Data Analysis (EDA)
• Google Cloud Platform (GCP)
• BigQuery
• Vertex AI
• Cloud Storage
• Cloud Functions
• Pandas
• NumPy
• Scikit-Learn
• TensorFlow OR PyTorch
• Statistical Analysis
• Predictive Modeling
• Business Stakeholder Management
• Requirement Gathering
• Root Cause Analysis
• Data-Driven Decision Making

Role: Data Science with GCP

Locations: Chennai

Employment Type: Full Time with NAM Info Pvt Ltd (Payroll)

Job Summary:

The ideal candidate should have strong expertise in Python| Machine Learning| Data Analysis| and Google Cloud Platform (GCP). This role requires close collaboration with business stakeholders to understand business challenges| translate requirements into actionable business cases| identify opportunities for improvement| and develop data-driven solutions that deliver measurable business value.

Responsibilities:

• Business & Stakeholder Management
• Engage with business stakeholders to understand business problems| goals| and requirements.
• Translate business requirements into clear analytical and machine learning use cases.
• Formulate business cases and define success metrics for proposed solutions.
• Collaborate with cross-functional teams including Product| Engineering| Operations| and Business teams.
• Data Analysis & Problem Identification
• Analyze existing applications| systems| and business processes to identify inefficiencies and improvement opportunities.
• Perform root cause analysis on business and operational challenges.
• Conduct exploratory data analysis (EDA) to discover trends| patterns| and anomalies.
• Generate actionable insights that support strategic business decisions.
• Machine Learning & Advanced AnalyticsDesign| develop| train| and deploy Machine Learning models.
• Implement predictive| classification| recommendation| forecasting| and anomaly detection solutions.
• Data Engineering & CloudWork with structured and unstructured datasets from multiple sources.
• Utilize GCP services such as:
• BigQuery
• Vertex
• AICloud Storage Cloud Functions

Technical Skills

• Strong proficiency in Python.
• Hands-on experience with: Pandas NumPyScikit-Learn TensorFlow/PyTorch, Matplotlib, Seaborn
• Strong understanding of: Machine Learning algorithms, Statistical analysis, Predictive modelling
• Hands-on experience with Google Cloud Platform (GCP).
• Knowledge of data visualization tools such as: Power BI/Tableau

Design, develop, and maintain high-performance Android applications using Kotlin (Java knowledge preferred). Integrate REST APIs, Firebase, WebSockets, payment gateways, and third-party services while implementing secure authentication (OTP, JWT, OAuth) and role-based access control. Optimize application performance, security, scalability, and manage Play Store releases. Experience with Git, cloud platforms (AWS/GCP), and API security best practices is essential. Exposure to real-time applications (ride-hailing, delivery, chat), CI/CD, Docker, and WhatsApp Business API is an advantage. Candidates should possess strong analytical, debugging, communication, and problem-solving skills with the ability to work independently in a fast-paced startup environment.

Amura’s Vision 

We believe that the most under-appreciated route to releasing untapped human potential is to build a healthier body, and through which a better brain. This allows us to do more of everything that is important to each one of us.

Billions of healthier brains, sitting in healthier bodies, can take up more complex problems that defy solutions today, including many existential threats, and solve them in just a few decades.

Billions of healthier brains will make the world richer beyond what we can imagine today. The surplus wealth, combined with better human capabilities, will lead us to a new renaissance, giving us a richer and more beautiful culture.

These healthier brains will be equipped with deeper intellect, be less acrimonious, more magnanimous, and have a kinder outlook on the world, resulting in a world that is better than any previous time.

We find this vision of the future exhilarating. Our hopes and dreams are to create this future as quickly as possible and ensure that it is widely distributed and optimized to maximize all forms of human excellence. 

Role Overview 

We are looking for a highly skilled Senior DevOps Engineer (AI-Native Infrastructure & Platform Engineering) with deep expertise in AWS cloud infrastructure, automation, AI infrastructure operations, and modern DevOps/SRE practices.

This role goes beyond traditional DevOps and requires a seasoned specialist capable of building and operating AI-ready infrastructure platforms that support high-throughput APIs, LLM/AI workloads, GPU-based compute, data-intensive systems, real-time inference pipelines, and scalable ML platforms.

You will be responsible for architecting, automating, securing, and optimizing highly scalable and cost-efficient cloud environments that enable high-velocity engineering and AI teams. This is an ideal position for someone who combines technical ownership, an automation-first mindset, and a passion for developer productivity and platform reliability. 

Key Responsibilities 

Cloud Infrastructure & Platform Engineering (AWS) 

• Architect, deploy, and manage highly scalable and secure infrastructure on AWS. Design cloud platforms supporting AI/ML workloads, data pipelines, real-time APIs, and high-concurrency backend systems.
• Hands-on expertise with key AWS services including EC2, ECS/EKS, Lambda, RDS, DynamoDB, S3, VPC, CloudFront, IAM, CloudWatch, and GPU-enabled instances.
• Build and maintain Infrastructure-as-Code (IaC) using Terraform, CloudFormation, or AWS CDK.
• Design multi-AZ and multi-region architectures for high availability and disaster recovery (HA/DR).
• Build reusable platform templates and shared infrastructure modules. 

AI/ML Infrastructure & MLOps 

• Build and maintain infrastructure for LLM applications, AI inference workloads, model serving platforms, vector databases, and feature stores.
• Support GPU-based workloads and optimize compute/storage usage.
• Enable scalable deployment patterns for AI applications using Kubernetes/EKS. Collaborate with Data Science and ML Engineering teams on model deployment, training/tuning of models, CI/CD for ML systems, experiment environments, and reproducibility.
• Support orchestration and deployment of AI workflows and inference services while implementing observability and reliability for AI pipelines. 

CI/CD, Automation & Developer Productivity 

• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipeline.
• Automate deployments, environment provisioning, and release workflows.
• Build self-service developer platforms, preview environments, and reusable deployment workflows to improve developer productivity.
• Implement automated patching, scaling, backups, cleanup workflows, and drift detection. 

Containers, Kubernetes & Platform Reliability

• Manage Docker-based environments, containerized applications, and optimize workloads using Kubernetes (EKS) or ECS/Fargate.
• Manage autoscaling, cluster health, node pools, ingress, service mesh, and workload isolation.
• Optimize infrastructure for performance, resilience, and cost-efficiency.
• Implement progressive deployment strategies including blue/green, canary, and rolling deployments. 

Observability, Incident Response & SRE Practices

• Implement observability stacks using CloudWatch, Prometheus, Grafana, ELK, Datadog, OpenTelemetry, or New Relic.
• Build actionable dashboards and intelligent alerting systems while defining and tracking SLIs, SLOs, and SLAs.
• Lead incident response, root cause analysis, and blameless postmortems to reduce operational toil and improve MTTR.

FinOps, Cost Governance & Security

• Continuously monitor and optimize cloud costs (compute utilization, storage lifecycle, GPU usage, and data transfer) using AWS Cost Explorer, Budgets, Trusted Advisor, CloudHealth, or Kubecost.
• Implement AWS security best practices for IAM, VPCs, security groups, NACLs, encryption, and manage secrets using KMS, SSM Parameter Store, or Vault.
• Build secure CI/CD pipelines with automated security checks, least-privilege access, audit logging, and ensure compliance readiness for ISO 27001, SOC2, and GDPR.

Collaboration, Leadership & Platform Culture

• Work closely with engineering, AI/ML, QA, product, and operations teams to drive a DevOps, SRE, GitOps, and automation-first culture.
• Mentor junior DevOps and Platform Engineers while creating and maintaining detailed runbooks, architecture diagrams, and platform documentation.

Skills & Qualifications

Must-Have:

• 7+ years of experience in DevOps, SRE, Platform Engineering, or Cloud Infrastructure Engineering.
• Strong expertise in AWS cloud architecture, services, and deep understanding of Kubernetes (EKS), containers, and cloud-native systems.
• Strong Infrastructure-as-Code expertise using Terraform, CloudFormation, or CDK. Strong Linux administration, networking, DNS, routing, and load balancing knowledge. Strong scripting/programming experience in Python, Bash, or Go (preferred). Experience with CI/CD automation, GitOps workflows, and observability platforms supporting scalable production systems.

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Preferred / Nice-to-Have:

• Experience with AI/ML infrastructure, MLOps, model serving, vector databases, GPU orchestration, and inference optimization.
• Familiarity with Kafka, Redis, SQS, and event-driven systems.
• Exposure to platform engineering, internal developer platforms, and tools like ArgoCD, Flux, Helm, and OpenTelemetry.
• AWS Certifications: Solutions Architect, DevOps Engineer, or SysOps Administrator. Knowledge of distributed systems and large-scale platform operations. 

Here are answers to some questions you may have

Where is your office?

Chennai (Velachery)

Work Model

Work from Office – because great stories are built in person!

Do you have an online presence?

https://amura.ai (we are @AmuraHealth on all social media)

• Data Preparation & Pipelines
• Collect, clean, structure and segregate data from multiple sources so it is ready for analysis and modelling; build repeatable data pipelines.
• Data accuracy & completeness; pipeline reliability; turnaround time.
• Model Development & Predictive Analytics
• Build, train, test and improve machine-learning models and prediction use cases.
• Model accuracy; validation rigour; improvement over baseline.
• Deployment, Integration & Documentation
• Serve models via APIs, support basic deployment, maintain version control and clear technical documentation. Build or integrate with backend services (Python-based or MEAN/MERN stack) to expose ML functionality to production applications.
• Solutions in actual use; clean repos; quality of documentation; smooth backend integration.
• Collaboration & Continuous Learning
• Work with the wider team and keep up with new tools, models and methods.
• Team feedback; adoption of new techniques; knowledge sharing.

Experience Required: 3-8 Years

No. of vacancies: 2

Job Type: Full Time

Vacancy Role: WFO

Job Category: Development

Job Description

We are looking for a Senior Python Developer to build and scale a production-grade AI-powered platform focused on image understanding, scene reconstruction, and automated media generation. This is a backend-heavy role requiring strong expertise in system design, asynchronous processing, and scalable API development. You will work closely with AI/ML, frontend, and DevOps teams to deliver high-performance systems that power object detection, segmentation, and 3D reconstruction pipelines.

Roles & Responsibilities

• Design and develop scalable backend services and APIs using FastAPI
• Build modular, maintainable systems with clear service contracts and abstractions
• Implement asynchronous processing pipelines for compute-heavy AI workloads
• Design and manage job queues (Celery, RQ, or similar) and integrate Redis
• Handle long-running and GPU-based tasks
• Work with PostgreSQL and integrate object storage (S3 or equivalent)
• Integrate and productionize ML models
• Optimize inference pipelines for latency, batching, and throughput
• Ensure system reliability with retries, fallbacks, and monitoring
• Collaborate with Data Science, Frontend, and DevOps team

Qualifications

• 3+ years of Python development experience
• Hands-on experience with FastAPI and REST APIs
• Strong understanding of async programming and scalable systems
• Experience with Redis and queue systems (Celery, RQ, Kafka)
• Strong PostgreSQL experience
• Experience with Docker (mandatory); Kubernetes is a plus
• Familiarity with AWS, GCP, or Azure
• Exposure to AI/ML pipelines (YOLO, DETR, SAM, etc.)
• Experience deploying ML models in production
• Understanding of GPU workloads and optimization
• Background in image/video processing or real-time systems
• Strong problem-solving and system design mindset
• Ability to handle real-world scale and unstructured data
• Experience in AI-integrated backend system
• 
  

About the Role :

As the Lead AI/ML Engineer, you will be responsible for leading the design, development, deployment, and continuous improvement of AI-powered solutions that create measurable impact in education. This role combines hands-on technical execution with engineering leadership, requiring you to architect scalable AI systems, mentor engineers, establish best practices, and collaborate closely with cross-functional teams to deliver production-grade solutions.

You will take ownership of the complete AI development lifecycle—from problem definition and data pipeline design to model training, deployment, monitoring, and optimization—while ensuring high standards of reliability, scalability, security, and performance. The role also involves evaluating emerging AI technologies and driving their adoption to enhance product capabilities and user experience.

Roles and Responsibilities:

• Lead the design, development, and deployment of AI-powered products for production environments.
• Drive technical architecture and decision-making across machine learning models, inference systems, cloud infrastructure, and distributed computing.
• Develop and maintain end-to-end machine learning pipelines, including data ingestion, model training, evaluation, deployment, monitoring, and continuous optimization.
• Design scalable, secure, and high-performance AI services capable of handling production-scale workloads.
• Provide technical leadership by mentoring engineers, conducting code and design reviews, and fostering engineering excellence.
• Establish and promote best practices for software quality, testing, performance optimization, observability, reliability, and maintainability.
• Collaborate closely with Product, Backend, Mobile, and Data teams to deliver robust, scalable, and business-focused AI solutions.
• Research, evaluate, and implement emerging AI technologies to enhance product capabilities and drive innovation.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical discipline.
• Minimum 4 years of hands-on experience developing, deploying, and maintaining production-grade AI/ML systems.
• Strong proficiency in Python with excellent software engineering, object-oriented programming, and system design skills.
• Extensive experience with modern machine learning frameworks such as PyTorch, Hugging Face, TensorFlow, or equivalent.
• Proven expertise in deploying, optimizing, and scaling large language models (LLMs) and other AI models for production inference.
• Strong understanding of containerization and cloud-native technologies, including Docker, Kubernetes, AWS, and distributed system architectures.
• Demonstrated ability to lead technical initiatives, mentor engineering teams, and make sound architectural decisions.
• Excellent analytical, debugging, communication, and problem-solving skills.

Preferred Qualifications

• Hands-on experience with Large Language Models (LLMs), Computer Vision, Speech Recognition, Generative AI, or Multimodal AI systems.
• Experience with model optimization and inference technologies such as Quantization, Distributed Training, vLLM, TensorRT, ONNX Runtime, or similar frameworks.
• Prior experience developing AI solutions for the education, public sector, or other large-scale social impact initiatives.

Details:

• Location: Kothrud, Pune.
• Interested candidates should fill in the application on career website   https://careers.vopa.in/                                 
• Salary :- 12 LPA to 14 LPA (Depending on last drawn salary, Interview and Skills)

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

Location: Bengaluru, India

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

Employment Type: Full-time

Company Overview

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

Position Summary

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

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

Key Responsibilities

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

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

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

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

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

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

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

• Provide thought leadership and strategic guidance within the organization.

Required Qualifications

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

Preferred Attributes

• Detail-oriented with a rigorous analytical mindset.

• Highly capable of conducting and guiding independent research.

• Strong written and verbal communication skills.

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

Why Join Big Air Lab?

• Lead groundbreaking research in cutting-edge AI technologies.

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

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

About Impact Analytics

Impact Analytics™ (Series D Funded) delivers AI-native SaaS solutions and consulting services that help companies maximize profitability and customer satisfaction through deeper data insights and predictive analytics. With a fully integrated, end-to-end platform for planning, forecasting, merchandising, pricing, and promotions, Impact Analytics empowers companies to make smarter decisions based on real-time insights rather than relying on last year’s inputs to forecast and plan this year’s business. Powered by over one million machine learning models, Impact Analytics has been leading AI innovation for a decade, setting new benchmarks in forecasting, planning, and operational excellence across the retail, grocery, manufacturing, and CPG sectors. In 2025, Impact Analytics is at the forefront of th eAgentic AI revolution, delivering autonomous solutions that enable businesses to adapt in real time, optimize operations, and drive profitability without manual intervention. Here’s a link to our website: www.impactanalytics.co.

The impact that you will be making

As a senior data scientist, you will help us discover the information hidden in vast amounts of data and help us make smarter decisions to deliver even better products. Primary focus will be in applying data mining techniques, performing statistical analysis, and building high quality prediction systems that can be integrated with our products.

What this role entail

● Understand and translate statistics and analytics to address client business problems.

● Apply Statistical forecasting algorithms to forecast client business needs for short term and long-term horizon.

● Explore Machine learning and Deep learning techniques to improve statistical Forecasting accuracy.

● Create business narrative by using storytelling and Visualization techniques and to present analytical insights to clients.

● Structure business problems and design solutions to meet client needs.

● Develop sophisticated analytical frameworks that add value to the client and result in new projects and revenue streams.

● Develop and implement analytical methodologies, processes, and technological solutions that integrate diverse information solutions and generate analytical insights.

What lands you in this role

● At least 3 years hands-on experience as a data scientist working on SQL, Python

● Must have exposure to developing predictive analytics and machine learning algorithms for business applications

● Strong forecasting and Deep Learning experience will be a plus

● Hands-on experience in relevant tools like SQL, Python, Tableau, etc.

● B Tech/ BE or equivalent degree in Data Science, Statistics, Computer Science, or similar

Some of our accolades include:

● Ranked as one of America's Fastest-Growing Companies by Financial Times for five consecutive years: 2020-2024.

● Ranked as one of America's Fastest-Growing Private Companies by Inc. 5000 for seven consecutive years: 2018-2024.

● Voted #1 by more than 300 retailers worldwide in the RIS Software Leaderboard 2024 report.

● Ranked #72 in America’s Most Innovative Companies list in 2023—by Fortune—alongside companies like Microsoft, Tesla, Apple, IBM, etc.

● Forged a strategic partnership with Google to equip retailers with cutting-edge generative AI tools.

● Recognized in multiple Gartner reports, including Market Guides and Hype Cycle, spanning assortments, merchandising, forecasting, algorithmic retailing, and Unified Price, Promotion, and Markdown Optimization Applications. Economic Times News about our funding can be accessed here.

Looking for somone with strong in AI, who have built the application and scaled them . Start up work exposure

8+ years of experience in successfully building, deploying, and running complex, large-scale web or data products.

Proven Management Experience: Demonstrated success managing a team of 5+ engineers for at least 2 years (managing timelines, performance, and hiring). You know how to transition a team from 'startup chaos' to 'structured agility'. 

● Full-stack Authority: Deep expertise with Javascript, Node.js, MySQL, and Python. You must have world-class expertise in at least one area but possess a solid understanding of the entire stack in a multi-tier environment. 

● Architectural Track Record: Has built at least two professional-grade products as the tech owner/architect and led the delivery of complex products from conception to release. 

● Experience in working with REST APIs, Machine Learning, Algorithms & AWS. 

● Familiar with visualization libraries and database technologies.

 ● Your reputation in the technology community within your domain. 

● Your participation and success in competitive programming. 

● Work on unusual/extraordinary hobby projects during school/college that were not a part of the curriculum. 

● The school that you come from and organizations where you have worked earlier. Personality Expectations We believe that it takes a certain type of personality to do a certain kind of role well. 

● Thoughtful & Analytical: Unlike a sales role, this role requires deep analytical ability and thoughtfulness. You don't just "hit goals at any cost"; you architect sustainable solutions that prevent future debt. 

● The "Pack Leader" Mentality: You are competitive, but you understand that your team's win is your win. You shift from getting a dopamine hit from solving a bug yourself to getting a hit from unblocking your team to solve ten bugs. 

● High Ownership of Outcomes: You don't just care that the code was written; you care that the feature was delivered, works for the customer, and didn't break production. You expect very highly of yourself and being less than ideal anywhere almost pains you. 

● Resilience: You possess the mental endurance to push through complex technical constraints and tight deadlines without losing your cool. 

● Uncompromising Values: On the other side, there is only one thing that we care for apart from performance - your values. We have room for mistakes on the performance side, we have no room for mistakes on your values. 

AI/ML Engineer AI Operating System for Capital Markets Location Bangalore/Chennai Experience 5+ years Function Artificial Intelligence / Machine Learning Employment Type About Transient.AI Full-time Transient.AI is building a next-generation AI Operating System for capital markets — a unified intelligence layer that connects research, trading, compliance, and sales functions at banks and hedge funds. Today, these teams largely operate on disconnected legacy systems, forcing manual, expensive workarounds. Transient.AI replaces that fragmentation with a single AI-native layer built for institutional-grade compliance, security, and auditability. The company already has live products in market, including Caddie.AI (a research automation tool that cuts hedge fund research time significantly), ClarityRIA (helping sales teams identify the right investors in seconds), and CapFlo.AI (automated parsing of complex derivatives contracts). Founded by former traders and technologists from Goldman Sachs, Credit Suisse, UBS, and McKinsey, Transient.AI is headquartered in New York, with teams in Miami, Singapore, and India. The company has raised Series A funding and is scaling its engineering and product organization globally. Role Overview Transient.AI is hiring an experienced AI/ML Engineer to join its India engineering team in Bangalore/Chennai. This is a hands-on, build-from-scratch role — you'll be designing and shipping the core machine learning systems that power the company's flagship products, working closely with founders and senior engineers rather than inheriting existing infrastructure. Key Responsibilities • Design, build, and deploy machine learning and AI models that power Transient.AI's core products (research automation, document intelligence, investor matching, and workflow orchestration). • Workonapplied NLP/LLMsystems, including retrieval-augmented generation, structured extraction from unstructured financial documents, and model evaluation pipelines. • Partner closely with product and founding engineers to translate capital markets workflows into scalable AI systems. • Ownmodelperformance, reliability, and cost — from experimentation through production deployment. • Build and maintain data pipelines, feature stores, and evaluation frameworks to support rapid iteration. • Ensuresystems meet the compliance, auditability, and security standards required in regulated financial environments. What We're Looking For • 5+years ofexperience building and deploying machine learning or AI systems in production.• Strong hands-on experience with Python and modern ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, or equivalent). • Experience with LLMs — fine-tuning, prompt engineering, RAG architectures, or agentic systems — is highly valued. • Solid grounding in data structures, distributed systems, and MLOps practices (model serving, monitoring, versioning). • Prior experience at a strong product company, high-growth startup, or a top-tier engineering background • Comfort operating in an early-stage, high-ownership environment with limited process and high ambiguity. • Exposure to fintech, capital markets, or other regulated industries is a plus, though not mandatory. WhyJoin Transient.AI • Build core AI systems from the ground up — not maintain legacy code. • Workdirectly with founders who have deep, first-hand Wall Street experience (Goldman Sachs, Credit Suisse, UBS, McKinsey). • JoinaSeries A-funded company solving a real, expensive problem for institutional finance. • Bepart ofasmall, global team with outsized ownership and impact. .

Machine Learning Engineer (For client company)

Location: Bengaluru, India (Hybrid/Onsite)

Experience: 3–4 years

The Role

We are looking for a Machine Learning Engineer to build and productionize models that power fall detection, vitals monitoring, and predictive health insights from radar sensor data.

You will work closely with hardware, data engineering, backend, and product teams to improve model accuracy, reduce false alarms, and deploy reliable ML systems into production.

This role is ideal for someone with strong classical machine learning fundamentals who is comfortable working with messy real-world sensor data and writing clean, production-grade code.

What You'll Do

• Build and optimize classical ML models such as XGBoost, ensemble models, anomaly detection, and time-series models for fall detection, vitals monitoring, and health risk scoring.
• Engineer features from raw, sparse, and noisy radar signals, point-cloud data, and time-series sensor streams.
• Contribute to computer vision-adjacent problems such as pose estimation, movement analysis, skeleton tracking, and activity recognition using radar data.
• Build training, evaluation, and inference pipelines using Databricks.
• Perform exploratory data analysis on resident, device, alert, and facility-level datasets to identify trends, edge cases, and opportunities for model improvement.
• Define and own model evaluation metrics for safety-critical systems, including:
• Precision
• Recall
• Sensitivity
• Specificity
• False alarm rate
• Missed event rate
• Detection latency
• Analyze production model performance across facilities, residents, devices, and time periods.
• Handle noisy real-world datasets, including:
• Missing values
• Label quality issues
• Device variability
• Sparse event data
• Facility-specific patterns
• Write clean, modular, well-tested Python code for feature engineering, model training, evaluation, and inference.
• Deploy, monitor, and continuously improve production ML models.
• Collaborate with hardware and data engineering teams to improve data quality, labeling, observability, and model reliability.

What We're Looking For

• 3–4 years of experience building and deploying machine learning systems in production.
• Strong Python programming skills with the ability to write maintainable, testable, production-grade code.
• Strong understanding of classical machine learning concepts, including:
• Feature engineering
• Model training
• Cross-validation
• Error analysis
• Model evaluation
• Hands-on experience with algorithms such as:
• XGBoost
• Random Forests
• Gradient Boosting
• Ensemble methods
• Anomaly Detection
• Time-series models
• Strong SQL skills with experience analyzing large datasets using SQL, PySpark, Pandas, or Databricks.
• Experience working with time-series, sensor, spatial, point-cloud, IoT, or computer vision-style datasets.
• Familiarity with modern data engineering workflows using Databricks, Apache Spark, Delta Lake, or similar platforms.
• Strong debugging and analytical skills with the ability to diagnose issues across data pipelines, models, and production systems.
• Comfortable working in a fast-moving startup environment with ambiguity.
• Strong ownership mindset with the ability to take ML models from experimentation through production deployment.

Good to Have

• Experience in HealthTech, IoT, radar sensing, wearables, ambient monitoring, or safety-critical systems.

Exposure to:

• Computer Vision
• Pose Estimation
• Skeleton Tracking
• Object Tracking
• Spatial Data Processing
• Experience with:
• MLflow
• Model Registry
• Feature Stores
• Experiment Tracking
• Model Monitoring
• Experience with:
• ONNX
• Model Quantization
• Edge Deployment
• Latency Optimization
• Resource-Constrained Inference
• Familiarity with real-time data pipelines using:
• Kafka
• Spark Structured Streaming
• Streaming inference architectures 

Job Summary

We are seeking an experienced Agentic AI Architect to design and build enterprise-scale Agentic AI platforms that enable autonomous, intelligent, and collaborative AI agents. The ideal candidate will drive architecture, technology strategy, and AI engineering best practices while delivering scalable, secure, and production-ready AI solutions.

Key Responsibilities

• Design enterprise architecture and technical roadmap for Agentic AI platforms.
• Build scalable multi-agent AI systems with reasoning, planning, memory, and autonomous decision-making capabilities.
• Architect solutions using LLMs, RAG, vector databases, embeddings, prompt engineering, orchestration frameworks, and agent execution engines.
• Design reusable frameworks for agent orchestration, lifecycle management, governance, observability, and monitoring.
• Integrate AI platforms with enterprise applications, APIs, databases, messaging systems, and cloud services.
• Implement AI security, governance, privacy, compliance, guardrails, and Responsible AI practices.
• Evaluate and implement frameworks such as Google ADK, LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, and MCP.
• Lead architecture reviews, technical governance, design workshops, and solution validation.
• Develop Proof of Concepts (PoCs) to evaluate emerging AI technologies.
• Mentor engineering teams and establish architecture standards and best practices.
• Collaborate with Product, AI/ML, Data Engineering, Cloud, DevOps, and Security teams.
• Drive AI platform scalability, modernization, performance optimization, and cost efficiency.

Required Skills

• Strong expertise in Agentic AI architecture and enterprise AI solution design.
• Hands-on experience with Large Language Models (GPT, Gemini, Claude, Llama, Mistral).
• Strong knowledge of RAG, Vector Databases, Embeddings, Prompt Engineering, AI Memory, Knowledge Graphs, and AI Orchestration.
• Experience with Google ADK, LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, MCP, or similar frameworks.
• Strong Python programming skills.
• Experience with FastAPI, REST APIs, Microservices, Distributed Systems, Event-Driven Architecture, Docker, Kubernetes, CI/CD, Git, and Azure/AWS/GCP.
• Knowledge of MLOps, LLMOps, AI Observability, monitoring, and production deployments.
• Strong understanding of AI Security, Governance, Responsible AI, and Enterprise Architecture.

Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Software Engineering, or a related field.
• 10+ years of software engineering experience.
• 5–7+ years of experience designing enterprise AI/ML platforms and distributed systems.
• Proven experience architecting production-grade Agentic AI solutions.

Preferred Experience

• Domain experience in Supply Chain, Manufacturing, Retail, Healthcare, Financial Services, or Enterprise Automation.
• Experience building multi-agent collaboration systems.
• Knowledge of AI Governance and Responsible AI.
• Enterprise Architecture and Cloud Certifications are preferred.

Mihup is a voice AI company building in-car voice assistance and sound technology for the automotive industry. We power intelligent voice experiences for leading OEMs.

Its solutions integrate automation, real-time assistance, and analytics at scale to enhance accuracy, efficiency, and agility in enterprise operations. Mihup.ai also serves organizations across BFSI, IoT, and other sectors, delivering robust conversation intelligence that supports performance and innovation. With over 1 billion interactions powered, Mihup.ai helps businesses turn voice data into measurable business outcomes and competitive advantage. The company offers a dynamic environment for professionals interested in advanced AI applications in automotive and other industries.

Role Description The AI Engineer will design, develop, and optimize machine learning models and algorithms that power Mihup.ai’s Voice AI and conversation intelligence solutions. Day-to-day responsibilities include building and refining pattern recognition systems, implementing and training neural networks, and developing NLP-based features that improve speech understanding and real-time assistance. The role involves collaborating with product, data, and engineering teams to integrate AI models into production-grade software, monitor performance, and iterate based on user data and business needs. The AI Engineer will also contribute to research, prototyping, and documentation to keep the platform aligned with best practices in AI and scalable software development. This is a full-time, on-site role based in Kolkata/ Bangalore

Qualifications

• Strong foundation in Computer Science, including data structures, algorithms, and software engineering principles.
• B. Tech, M.Tech, PHD from top tier institutes like IIT, IISC, ISI, NIT, IIIT is mandatory.
• Demonstrated experience with Pattern Recognition and Neural Networks for real-world applications.
• Hands-on expertise in Natural Language Processing (NLP), especially in speech, dialogue, or text analytics.
• Proficiency in Software Development using languages such as Python, Java, or similar, and familiarity with version control and CI/CD.
• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and relevant libraries for NLP and deep learning.
• Experience working with large-scale data pipelines, model evaluation, and performance optimization.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical discipline, or equivalent practical experience.
• Ability to work collaboratively in cross-functional teams, communicate technical concepts clearly, and document solutions effectively.
• Background in voice technologies, speech recognition, or conversational AI is a strong plus.

Supercharge Your Career as a AI DevOps Engineer at Technoidentity!

At Technoidentity, we're a Data & AI product engineering company with over 15 years of expertise in building durable digital products, intelligent enterprise solutions, and scalable Data & AI platforms. As we continue expanding globally, it's the perfect time to join our team of tech innovators and make a lasting impact.

What’s in it for You?

We are looking for an AI DevOps Engineer with 0–3 years of experience who is passionate about AI, Cloud, DevOps, and Automation. The role involves building, deploying, and managing AI-powered applications, LLM solutions, and cloud-native platforms while ensuring reliability, scalability, security, and observability.

What Will You Be Doing?

• Develop and deploy AI/ML and Generative AI solutions using Python.
• Build applications leveraging LLMs, RAG, and AI agents.
• Create and maintain CI/CD pipelines for AI applications.
• Deploy and manage workloads using Docker and Kubernetes.
• Support cloud platforms (AWS, Azure, or GCP).
• Implement Infrastructure as Code (Terraform) and automation workflows.
• Monitor applications using observability tools such as Prometheus, Grafana, and logging platforms.
• Collaborate with engineering teams to ensure system reliability, performance, and security.
• Contribute to MLOps practices, AI accelerators, and reusable frameworks.

Requirements

What Makes You the Perfect Fit?

• Python programming (mandatory)
• Understanding of Machine Learning, LLMs, Prompt Engineering, and RAG
• Experience with OpenAI, LangChain, LlamaIndex, or Hugging Face
• Docker, Kubernetes, Git, and CI/CD tools
• AWS, Azure, or GCP
• PostgreSQL; MongoDB and Vector Databases are a plus
• Basic knowledge of MLOps, Terraform, and workflow orchestration tools (Airflow/Temporal)
• Familiarity with observability and monitoring tools

Qualifications

• Bachelor's degree in Computer Science, AI, Data Science, IT, or related field
• 0–3 years of experience in AI/ML, Software Engineering, Cloud, DevOps, or related areas

Nice to Have

• Experience with Agentic AI frameworks
• Knowledge of MLOps and AI platform operations
• Exposure to enterprise-grade monitoring, reliability engineering, and security best practices

About Ritually

Ritually is building the definitive process discovery platform for back office work. Our product fuses underutilized system telemetry with computer vision to help large enterprise and scaling mid-market companies deeply understand and reimagine their highest value and most repetitive processes for a world where humans and agents work together. We're based in New York and Denver.

We believe deeply in trust (of our customers and each other), craft, customer obsession, and speed.

You'll be joining an AI-native, fast-moving, and repeat founding team. Ritually's founders previously built and exited a startup (Involvio) to Cisco. The company is funded and working with design partners.

The Role

This is a founding applied-AI role. You'll be building our core data pipeline and intelligence layer with the founding team from 0-1 You'll be tackling our largest technical challenges across technologies.

What You'll Do

• Design and build data pipelines that capture and turn high volumes of system activity into structured, queryable data.
• Turn raw activity streams into processes: sessionize event logs, cluster recurring sequences, and use LLMs to label and summarize what's happening.
• Build the evaluation backbone from scratch: stand up synthetic data generation pipelines that produce labeled scenarios to measure accuracy and catch regressions.
• Own data quality and privacy.
• Partner closely with the founders and the rest of engineering to ship features end to end.

What We're Looking For

• 1-4 years of experience in data engineering, AI/ML engineering, or backend work with a data focus (some of this can be project or research experience).
• Strong in Python and/or TypeScript, comfortable in SQL, and able to build data pipelines you can trust.
• Hands-on experience working with LLMs structured output, prompting, and wrangling non-determinism while keeping behavior reliable.
• A practical sense for evaluation: you know that "it looks right" isn't the same as "it's measurably right."
• Care about data privacy and handling sensitive information responsibly.
• Comfort with ambiguity and a real appetite to own a hard, open-ended problem.

Nice to Have

• Background in process mining, sequence / event-log analysis, or workflow analytics.
• Deeper PostgreSQL: window functions, partitioning, pg_cron, query performance.
• Embeddings and vector search (pgvector) or semantic retrieval.
• Familiarity with cloud infrastructure.
• Any prior early-stage startup experience.
• Degree in computer science or a related field.

About the Internship

SkillSecure X is seeking passionate Artificial Intelligence & Machine Learning (AI/ML) Interns who are eager to develop practical skills in machine learning, deep learning, and AI applications. Interns will work on guided projects, gain hands-on experience with real-world datasets, and learn industry-standard AI tools and techniques.

Responsibilities

• Assist in developing AI and Machine Learning models.
• Work with Python and popular ML libraries.
• Prepare, clean, and analyze datasets.
• Perform data preprocessing and model evaluation.
• Build basic predictive and classification models.
• Document project progress and complete weekly assignments.

Required Skills

• Basic knowledge of Python programming.
• Understanding of Machine Learning fundamentals.
• Familiarity with Data Science concepts.
• Knowledge of libraries such as NumPy, Pandas, or Scikit-learn is a plus.
• Strong analytical and problem-solving skills.

Eligibility

• Undergraduate or postgraduate students.
• Recent graduates.
• Students pursuing Computer Science, Artificial Intelligence, Data Science, Information Technology, Engineering, or related disciplines.

Benefits

• Hands-on AI & ML project experience.
• Mentor-guided learning.
• Internship Completion Certificate.
• Letter of Recommendation (based on performance).
• Portfolio-building opportunities.
• Flexible remote internship.

Job Title: Associate AI/ML Engineer

Location: Gurugram, Haryana

Employment Type: Full-Time

About Aaizel Tech

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

Role Overview

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

Key Responsibilities

1. Model Development & Optimization

Design & Implementation:

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

Optimization:

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

2. Advanced Data Engineering & Integration

Data Pipeline Development:

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

Data Quality & Preprocessing:

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

3. Research & Innovation

Algorithm Research:

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

Prototyping & Experimentation:

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

4. Deployment, MLOps & Performance Monitoring

Model Deployment:

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

MLOps & Lifecycle Management:

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

5. Collaboration & Leadership

Cross-Functional Teamwork:

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

Mentorship & Code Quality:

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

Recommended Technology Stack

Backend Framework:

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

AI/ML Frameworks:

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

Data Engineering:

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

Database & Storage:

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

DevOps & Monitoring:

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

Media Processing:

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

Required Skills & Qualifications

Technical Expertise:

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

Soft Skills:

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

Educational Background:

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

What We Offer

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

About Us

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

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

Role Overview

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

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

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

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

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

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

Senior AI/ML Engineer

Location: Gurugram, Haryana

Employment Type: Full-Time

Experience: 5+ Years

About Aaizel Tech

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

Role Overview

As AI/ML Engineer, you will lead the development, deployment, and optimization of advanced AI systems spanning Generative AI, Large Language Models (LLMs), Agentic AI, Computer Vision, and intelligent automation. You will drive innovation, mentor junior engineers, and build scalable, production-ready AI solutions.

Key Responsibilities

AI Development & Research

• Design and develop end-to-end AI/ML solutions using Machine Learning, Deep Learning, and Generative AI techniques.
•  
• Build solutions involving NLP, Computer Vision, multimodal AI, anomaly detection, and predictive analytics.
•  
• Develop and optimize LLM-based applications, RAG pipelines, and Agentic AI workflows.
•  
• Fine-tune and evaluate foundation models, including LLMs and Vision Language Models (VLMs).
•  
• Research, prototype, and productionize state-of-the-art AI techniques.

Model Optimization & Deployment

• Optimize models for performance, scalability, latency, and real-time inference.
•  
• Apply model compression techniques such as quantization, pruning, and distillation for edge and cloud deployments.
•  
• Deploy AI applications using Docker, Kubernetes, and modern model-serving frameworks.
•  
• Implement MLOps/LLMOps practices, CI/CD pipelines, model versioning, monitoring, and observability.

Collaboration & Leadership

• Collaborate with cross-functional teams to integrate AI capabilities into end-to-end products.
•  
• Mentor junior engineers, conduct code reviews, and promote engineering best practices.
•  
• Maintain technical documentation and contribute to architectural decisions.

Preferred Technology Stack

Programming: Python, SQL

AI/ML: PyTorch, TensorFlow, Scikit-learn, Hugging Face, PyTorch Lightning

Generative AI & Agents: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, DSPy

LLMs & RAG: OpenAI, Gemini, Anthropic APIs, FAISS, Pinecone, Milvus, Qdrant, Weaviate

Computer Vision & Edge AI: OpenCV, ONNX Runtime, TensorRT, OpenVINO, Tesseract OCR

MLOps/LLMOps: Docker, Kubernetes, MLflow, Kubeflow, Weights & Biases, GitLab CI/CD

Cloud: AWS, Azure, GCP

Monitoring: Prometheus, Grafana, LangSmith, Evidently AI

Databases: PostgreSQL, MongoDB, Redis

Required Skills & Qualifications

• 5+ years of experience in AI/ML, Deep Learning, or related domains.
•  
• Strong proficiency in Python and hands-on experience with PyTorch, TensorFlow, and Hugging Face ecosystem.
•  
• Experience building LLM applications, RAG systems, and Agentic AI solutions.
•  
• Strong understanding of Generative AI, Transformers, Prompt Engineering, and modern NLP techniques.
•  
• Experience in Computer Vision and deploying optimized models in cloud and/or edge environments.
•  
• Hands-on experience with Docker, Kubernetes, CI/CD pipelines, and cloud platforms.
•  
• Proven ability to translate research into scalable production systems.
•  
• Excellent problem-solving, communication, leadership, and mentoring skills.

Education

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.

What We Offer

• Opportunity to work on cutting-edge AI and Generative AI solutions.
• Continuous learning, research, and career growth opportunities.
• Collaborative and innovation-driven work culture.
• Opportunity to build impactful, next-generation AI products.

How to Apply

• Please submit your resume, portfolio, and a cover letter outlining your relevant experience and how you can contribute to Aaizel Tech’s success.

Job Description

Our Media Measurement team uses state-of-the-art technologies and rigorous methods to track who is watching what, where, and how they engage with content. Our clients can evaluate who is consuming which content across different media, platforms and devices, and know what the audience thinks about that content. As people consume media content on more channels, and through more devices, than ever before, we are proud to provide a full view on media consumption.

As Data Scientist you will have following main accountabilities: 

• You own together with your team several of our Data Science solutions throughout the full life cycle (brainstorming, design, implementation, productization and maintenance) 
• You develop solutions based on data science, stats and machine learning models 
• You improve methods and tools. Contribute to our communities of practice in the area of Data Science 
• Communicate with non-data scientists in Tech, Operations, Commercial, Product. Understand the domain and the requirements. Explain Data Science principles, concepts, algorithms, and approaches in simple words to different types of audiences 
• Make data your best friends. Understand their strengths and use them. Be aware of their weaknesses and handle those in your solutions 
• Screen the market for potential new Data Science approaches 
• Foster knowledge exchange within the company. Present GfK's Data Science expertise at conferences and workshops 

Qualifications

Now you know what a Data Scientist does. What skills, qualifications & experience do you need for this job? 

• You typically have a Master's degree or PhD that reflects modeling and statistics skills and 6+ years of experience. 
• You enjoy communicating complex methodology and technology to tech and non tech audiences 
• You have expert statistical / machine learning modeling skills (e.g. statistical tests, classification, predictive modelling, handling of missing data, sampling, weighting) 
• You have experience with an analytical programming language (Python) and the respective ecosystem 

Besides the things we really expect you to have, there are some things which would be amazing if you have experience with them: 

• Knowledge of cloud computing environments and tooling (especially AWS) 
• Advanced software development skills (unit testing, CI/CD, Git) 
• Basic skills regarding database handling as SQL 
• Basic knowledge of the always evolving Data Science ecosystem and its frameworks 

Additional Information

• Enjoy a flexible and rewarding work environment with peer-to-peer recognition platforms. 
• Recharge and revitalize with help of wellness plans made for you and your family. 
• Plan your future with financial wellness tools. 
• Stay relevant and upskill yourself with career development opportunities. 

Our Benefits

• Flexible working environment
• Volunteer time off
• LinkedIn Learning
• Employee-Assistance-Program (EAP)

About the Role

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

Domains & Responsibilities 

Frontend Development 

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

 Backend Development 

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

 DevOps 

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

 AI / Machine Learning 

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

 Data Engineering 

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

 Required Skills (Any Domain) 

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

 Good to Have 

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

 Who Should Apply 

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

 Internship Details 

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

 What You’ll Gain 

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

Junior Data Scientist (2–3 Years Experience)

• Strong understanding of Probability & Statistics
• Strong educational background in Statistics or Mathematics (degree/course specialization). 
• Knowledge of core Machine Learning algorithms
• Proficiency in Python
• Experience with Natural Language Processing (NLP)
• Understanding of Transformer-based models
• 2–3 years of hands-on Data Science experience
• Good analytical and problem-solving skills

Senior Data Scientist (ML) – 5+ Years

Required Skills:

• 5+ years of experience in Data Science/ML
• Strong knowledge of Probability & Statistics
• Strong educational background in Statistics or Mathematics (degree/course specialization). 
• Good understanding of core Machine Learning algorithms
• Proficiency in Python, PySpark, and SQL
• Experience with Databricks, Azure ML, SageMaker, or Vertex AI
• Hands-on experience in Natural Language Processing (NLP)
• Understanding of Transformer-based models and architectures
• Ability to build, deploy, and optimize ML solutions at scale

Experience: 6+ years building and operating production ML systems that drive commercial decisions at scale. 

Location: Remote 

To streamline and fast-track screening, please submit your details here (if you haven’t already): https://airtable.com/appbtkr4odapnb5I6/pag05ROZwgz5AaLDG/form 

We’ll review your responses as part of the initial screening process. Please make sure you complete and submit all details through the form to be considered for the next stage. Submissions outside the form may not be considered.

Why This Role Matters

Terrabase builds decisioning infrastructure for enterprise customers: ranked recommendations, scoring pipelines, and policy-governed outputs that drive real commercial action. Our ML systems do not live in notebooks. They run multi-stage evaluation harnesses, apply structured governance rules, backtest against historical outcomes, and ship ranked outputs that customers act on daily.

This role owns the decisioning system end to end. That means the models, the eval harness, the policy layer, the production services, and the technical roadmap for where all of it goes next.

What You Will Do

Own the decisioning and ranking pipeline. Design, extend, and operate the end-to-end system: candidate generation in DuckDB, multi-stage scoring with LightGBM and AutoGluon, post-score policy application, and final ranked output delivery. You understand each layer well enough to debug latency, correctness, and coverage problems quickly, and to design the next version.

Lead the evaluation harness. Our eval pipeline runs multiple gates before any output ships: data health checks, specification validation, business rules enforcement, resolution checks, LLM-as-judge scoring, backtest against historical outcomes, and final output validation. You will own this harness, extend it as the system grows, and ensure every model or pipeline change is measurable and reproducible before it reaches a customer.

Apply policy logic with rigor. Our ML systems operate under structured governance rules that determine which offers apply to which customer segments, under what conditions. You will implement, test, and audit these rules in code, not configure them in a spreadsheet. Every exclusion must be traceable and explainable.

Engineer features that move metrics. Identify and build the behavioral signals, engagement indicators, contract features, and value-band attributes that improve model performance. Close the loop from feature hypothesis through offline evaluation to production monitoring. Own the data contracts between upstream sources and the scoring pipeline.

Build and maintain the production pipeline and service layer. The decisioning system is not a batch notebook. You will write and operate the Python pipeline and service layer that wraps model inference, handles edge cases, versions model artifacts, and connects to downstream consumers. You own CI, test coverage, reproducible training runs, monitoring, and production incidents.

Drive technical direction. Write design documents, lead code review, and set the engineering standard for the decisioning system. Help define the roadmap: what gets built, in what order, and why. Mentor contributors who work alongside you on this system.

Work forward-deployed. You will engage directly with customer stakeholders to understand business context, interpret model outputs, and translate commercial requirements into system constraints. You are accountable for customer delivery, not just model accuracy.

What We Are Looking For

• 6+ years building and operating production ML systems, not prototypes or research work
• Strong Python skills across the full ML lifecycle: data pipelines, feature engineering, model training, inference services, and monitoring
• Production experience with gradient boosting models (LightGBM, XGBoost)
• Hands-on with DuckDB or similar in-process analytical engines for large-scale data processing
• Evaluation discipline: held-out metrics, backtesting against historical data, multi-gate eval pipelines, LLM-as-judge patterns
• Experience applying business rules, policy overrides, or constraint layers on top of model outputs
• Engineering fundamentals: CI pipelines, data contracts, versioned artifacts, test coverage, incident response
• Technical leadership: design docs, code review, roadmap input, mentoring
• Comfort with forward-deployed work: you can run a meeting with a non-technical stakeholder and turn the output into a system requirement
• Comfort inheriting an existing production codebase, improving its structure, and raising reliability without rewriting everything from scratch

Bonus Points

• Experience with next-best-offer engines, customer-level targeting, or recommendation systems at scale
• Experience with AutoML frameworks (AutoGluon or similar) in a production scoring pipeline
• Thompson sampling, multi-armed bandits, or portfolio-level optimization experience
• Exposure to structured data from telecoms, financial services, or retail sectors
• Prior work owning a decisioning or ranking system as the technical lead

Life at Terrabase

We are a sharp, focused, fully remote team that ships to real enterprise customers weekly. You will own a system that drives measurable commercial outcomes, with high autonomy, generous cloud budgets, and a culture that prizes rigor over hype.

Terrabase is an equal-opportunity employer. We celebrate diversity and are committed to building an inclusive environment for every team member.

ey Responsibilities

✅ Architect, develop, and deploy AI-powered enterprise applications and intelligent automation solutions.

✅ Translate business requirements into scalable, secure, and production-ready AI systems.

✅ Build and integrate AI/ML capabilities into enterprise platforms and digital products.

✅ Design cloud-native applications leveraging AWS, Azure, or Google Cloud Platform (GCP).

✅ Develop APIs, microservices, distributed systems, and modern integration frameworks.

✅ Implement DevOps best practices, CI/CD automation, and engineering lifecycle processes.

✅ Utilize AI-assisted development tools to improve productivity and accelerate delivery.

✅ Ensure compliance with security standards, governance frameworks, and data privacy requirements.

✅ Collaborate with cross-functional teams, product stakeholders, and business leaders to deliver innovative solutions.

Required Skills

🔹 5+ years of experience in Software Engineering, Solution Architecture, or Enterprise Application Development.

🔹 Strong programming expertise in *Python, Java, TypeScript, or Go*.

🔹 Hands-on experience integrating AI/ML solutions into enterprise applications.

🔹 Experience with cloud platforms such as *AWS, Azure, or GCP*.

🔹 Strong understanding of *APIs, Microservices, Distributed Systems, and Containerization*.

🔹 Experience with *Agile methodologies, DevOps practices, and CI/CD pipelines*.

🔹 Knowledge of secure software development and enterprise governance standards.

🔹 Excellent problem-solving, communication, and stakeholder management skills.

Good to Have

⭐ Experience with *Machine Learning, Generative AI, Intelligent Automation, or LLM-based solutions*.

⭐ Knowledge of *MLOps, Prompt Engineering, Model Monitoring, AI Validation, and LLM Integration*.

⭐ Experience in *Banking, FinTech, Payments, or other regulated industries*.

⭐ Advanced degree in Computer Science, Artificial Intelligence, Engineering, or related fields.

⭐ Certifications such as *AWS/Azure/GCP Solutions Architect, TOGAF, CKA, or Lean Six Sigma*.

⭐ Experience leading engineering modernization, transformation, or automation initiatives.

⭐ Contributions to open-source projects and a passion for emerging technologies.

Job description:

Interview Venue: H-28, ARV Park, Sector 63, Noida, Uttar Pradesh 201301

Job location: Noida

Experience: 1-2 years

Education: B.Tech or MCA

The procedure for the interview will be as follows:

• 1st Round - Machine Round
• 2nd Round- Communication Round
• 3rd Round – Assignment Round
• 4th Round - HR Round

Job Description:

• Profile: AI/ML Engineer
• Experience: 1-2 yrs
• Qualification : B.Tech/MCA
• Working Days: 5
• Job Nature: Permanent

Roles And Responsibilities:

1) Python Proficiency and API Integration:

Demonstrate strong proficiency in Python programming language.

Design and implement scalable, efficient, and maintainable code for machine learning applications.

Integrate machine learning models with APIs to facilitate seamless communication between different software components.

2) Machine Learning Model Deployment, Training, and Performance:

Develop and deploy machine learning models for real-world applications.

Conduct model training, optimization, and performance evaluation.

Collaborate with cross-functional teams to ensure the successful integration of machine learning solutions into production systems.

3) Large Language Model Understanding and Integration:

Possess a deep understanding of large language models (LLMs) and their applications.

Integrate LLMs into existing systems and workflows to enhance natural language processing capabilities.

Stay abreast of the latest advancements in large language models and contribute insights to the team.

4) Langchain and RAG-Based Systems (e.g., LLamaindex):

Familiarity with Langchain and RAG-based systems, such as LLamaindex, will be a significant advantage.

Work on the design and implementation of systems that leverage Langchain and RAG-based approaches for enhanced performance and functionality.

5) LLM Integration with Vector Databases (e.g., Pinecone):

Experience in integrating large language models with vector databases, such as Pinecone, for efficient storage and retrieval of information.

Optimize the integration of LLMs with vector databases to ensure high-performance and low-latency interactions.

6) Natural Language Processing (NLP):

Expertise in NLP techniques such as tokenization, named entity recognition, sentiment analysis, and language translation.

Experience with NLP libraries and frameworks like NLTK, SpaCy, Hugging Face Transformers

7) Computer Vision:

Proficiency in computer vision tasks such as image classification, object detection, segmentation, and image generation.

Experience with computer vision libraries like OpenCV, PIL, and frameworks like TensorFlow, PyTorch, and Keras.

8) Deep Learning:

Strong understanding of deep learning concepts and architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Proficiency in using deep learning frameworks like TensorFlow, PyTorch, and Keras.

Experience with model optimization, hyperparameter tuning, and transfer learning.

9) Data Manipulation:

Strong skills in data manipulation and analysis using libraries like Pandas, NumPy, and SciPy.

Proficiency in data cleaning, preprocessing, and augmentation techniques.

Perks&Benefits:

• Employees & Family Health Insurance, EPF & ESIC.
• Free late-night meal facility.
• Innumerable in house & outdoor party.
• Various compensations & bonuses.
• No dress Code.
• Festival Celebration.
• Employees' B'day celebration.