Role & responsibilities

- Design, develop, and deploy AI-powered applications using Large Language Models (LLMs) and Generative AI frameworks.

- Build AI agents using LangGraph, AutoGen, CrewAI, or PydanticAI for workflow automation and intelligent decision-making.

- Develop and optimize Retrieval-Augmented Generation (RAG) solutions and prompt engineering strategies for enterprise use cases.

- Integrate LLMs such as GPT, Claude, and Llama into scalable business applications.

- Deploy and manage AI/ML models using Azure Machine Learning and Databricks platforms.

- Build and maintain REST APIs, Web Sockets, and event-driven architectures for real-time AI services.

- Implement ML Ops best practices, including model versioning, monitoring, CI/CD pipelines, and automated deployments.

- Collaborate with cross-functional teams to ensure secure, scalable, and reliable AI solutions in production environments.

- Write high-quality Python code and follow modern software development practices, SDLC processes, and Agile methodologies.

- Utilize development tools such as Git, Jenkins, Jira, Confluence, and AI-assisted coding tools to improve productivity and software quality.

- Monitor model performance, troubleshoot production issues, and continuously enhance AI solution effectiveness.

- Contribute to enterprise AI governance, security, compliance,



and best practices for AI system lifecycle management.

Preferred candidate profile

- 6-8 years of experience in Artificial Intelligence, Machine Learning, or Data Science with strong hands-on development expertise.

- Minimum 1+ year of experience building Generative AI applications and working with LLMs in enterprise environments.

- Strong experience in LangGraph, CrewAI, AutoGen, or PydanticAI for AI agent development and orchestration.

- Hands-on expertise in RAG (Retrieval Augmented Generation), Prompt Engineering, Vector Databases, and LLM Workflows.

- Proficient in Python with robust coding, debugging, and software engineering practices.

- Experience with Azure Machine Learning, Databricks, and Cloud-based AI Deployments.

- Strong knowledge of MLOps/LLMOps, model versioning, monitoring, and CI/CD pipelines.

- Experience developing and integrating REST APIs, WebSockets, and Event-Driven Architectures.

- Familiarity with Git, Jenkins, Jira, Confluence, SDLC, and Agile methodologies.

- Experience using AI-assisted development tools such as GitHub Copilot, Windsurf, or Codeium.

- Good understanding of enterprise security, governance, compliance, and AI best practices.

- Excellent communication, stakeholder management, analytical thinking, and problem-solving skills.

📌 AI/ML Engineer - GenAI, Lang Graph, Azure ML, Databricks (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru