Key Responsibilities:

Agent Development & Architecture:

- Build agentic workflows using LangChain/LangGraph and similar frameworks. • Develop autonomous agents for data validation, reporting, document processing, and domain workflows
- Deploy scalable, resilient agent pipelines with monitoring and evaluation

GenAI Application Engineering

- Develop GenAI applications using models like GPT, Gemini, and LLaM
- Implement RAG, vector search, prompt orchestration, and model evaluation
- Partner with data scientists to productionize POCs

Data & Platform Engineering

- Build distributed data pipelines (Python, PySpark)
- Develop APIs, SDKs, and integration layers for AI-powered applications
- Optimize systems for performance and scalability across cloud/hybrid environments.

MLOps / LLMOps

- Contribute to CI/CD workflows for AI models-deployment, testing, monitoring
- Implement governance, guardrails, and reusable GenAI frameworks.

Collaboration & Stakeholder Engagement

- Work with analytics, product, and engineering teams to define and deliver AI solutions
- Participate in architecture reviews and iterative development cycles
- Support knowledge sharing and internal GenAI capability building.

Key Requirements:





- 8-12 years of experience in AI/ML engineering, data science, or software engineering, with atleast 2 years focused on GenAI
- Solid programming expertise in Python, distributed computing using PySpark, and API development
- Hands on experience with LLM frameworks (LangChain, LangGraph, Transformers, OpenAI/Vertex/Bedrock SDKs)
- Experience developing AI agents, retrieval pipelines, tool calling structures, or autonomous task orchestration
- Solid understanding of GenAI concepts: prompting, embeddings, RAG, evaluation metrics, hallucination identification, model selection, fine tuning, context engineering
- Experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), and CI/CD pipelines for ML/AI
- Strong problem solving, system design thinking, and ability to translate business needs into scalable AI solutions
- Excellent verbal, written communication and presentation skills. Good to Have
- Experience in workflow automation and building reusable AI components
- Background in analytics, statistical models, or enterprise data products
- Experience with MLOps / LLMOps tooling

📌 Data Scientist (Chennai)
🏢 Dun u0026 Bradstreet India
📍 Chennai