Job Description:

- Design, build, and optimize end-to-end AI systems leveraging both LLMs and classical ML models.

- Develop GenAI-powered applications using frameworks such as LangChain, LangGraph, LangSmith, Langfuse, LlamaIndex and agentic architectures for workflow automation, with observability and traceability.

- Develop Knowledge Graphs and leverage Graph DBs and Graph RAGs to implement grounded AI solutions.

- Implement advanced RAG pipelines - including document chunking, vector database integration, semantic search and hybrid search. Experience in RAG optimization techniques (query rewriting, reranking, memory-augmented approaches).

- Engineer and refine prompts, system templates, and chains for performance, consistency, and contextual reasoning.

- Work with Rest APIs, well-versed in API integration with any stack UI.

- Design and manage multi-agent orchestration systems that interact and collaborate across tasks.

- Develop, evaluate, and deploy traditional ML models using Python, Pandas, NumPy, Scikit-learn, and other libraries.

- Work closely with data engineers to ensure data readiness, feature engineering, and model lifecycle governance.

- Deploy models using Azure ML, Azure OpenAI, Cognitive Services, and other Azure AI offerings.