1. Python expert ; APIs
2. GHA ; YAML ; build/deploy pipelines ; Docker ; AWS
3. LLMs ; Chatbots ; OpenAI ; tool calling ; LangChain ; LangGraph ; Agentic architecture ; RAG
4. Java ; Spring
5. React ; JavaScript

• LLM, RAG & Agentic Framework

Key Responsibilities:

Design and implement Retrieval-Augmented Generation (RAG) pipelines using LangChain and LangGraph.

Integrate AWS Open Source Vector Databases (e.g., OpenSearch with KNN plugin or other OSS-compatible vector stores).

Handle complex query chaining, prompt orchestration, and data retrieval using custom agents and tools.

Work on graph-based knowledge representation and retrieval using technologies like Neo4j or Stardog, or similar (preferred).

Collaborate with data science and engineering teams to enable end-to-end use case delivery.

Optimize performance for multi-document, multi-hop reasoning over structured/unstructured data.

Required Skills:

• Strong hands-on experience with Python, LangChain and LangGraph
• Experience implementing RAG pipelines using open-source vector stores
• Familiarity with Graph Databases and GraphQL
• Good understanding of LLMs, embeddings, and prompt engineering
• Experience with AWS services (S3, Lambda, ECS/EKS, etc.)
• Familiarity with FastAPI or Flask for API deployments
• Good to heve Version control and DevOps exposure (Git, Docker, CI/CD pipelines)