Key Responsibilities :

- Design and deploy agentic generative AI solutions leveraging LLMs, RAG pipelines, context engineering, and REST APIs.

- Architect and build multi-agent automation systems using frameworks such as LangGraph or similar orchestration platforms

- Implement LLM governance architecture including prompt drift detection, auditability, and safety policies to ensure regulatory compliance.

- Develop and integrate vector database solutions for semantic search and retrieval-augmented generation.

- Collaborate cross-functionally with AI architects, product managers, and compliance teams to accelerate AI adoption across business units.

- Develop APIs using FastAPI/Flask and build interactive demos for stakeholder showcases.

- Contribute to MLOps practices including CI/CD pipelines, model monitoring, and deployment automation.

- Conduct research experiments to enhance model performance, reliability, and fairness.

- Build and maintain ML models for domain-specific tasks such as NLP, computer vision, and predictive analytics.

Required Qualifications :

Technical Skills :

- Programming : Proficiency in Python; working knowledge of SQL and Java.

- GenAI & LLMs : Deep expertise in LLMs, RAG, prompt engineering, fine-tuning, and model governance.

- Frameworks : LangChain,

- APIs & Backend : FastAPI, Flask, REST API design.

- Databases : Vector databases (Milvus, PGVector), PostGRESQL.

- Cloud & MLOps : Azure AI Foundry; Docker, Kubernetes, CI/CD pipelines.

- Visualization & UI : Streamlit, basic UI/UX design principles.