Job Summary
We are looking for an experienced
Agentic AI / RAG / LLM Engineer
to design and develop intelligent AI applications using Large Language Models, Retrieval-Augmented Generation, and AI agents. The ideal candidate should have strong hands-on experience in
Python,
Key Responsibilities
- Design and develop Agentic AI applications using LLMs and autonomous AI agents.
- Build RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
- Develop multi-agent and tool-based workflows using LangGraph, LangChain, CrewAI, AutoGen, or similar frameworks.
- Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, Llama, or Hugging Face models.
- Implement function/tool calling, workflow orchestration, memory, planning, and reasoning capabilities.
- Work with vector databases such as Pinecone, FAISS, Weaviate, Milvus, Chroma, or pgvector.
- Develop scalable AI services and APIs using Python and FastAPI.
- Implement prompt engineering, structured outputs, context management, and LLM evaluation.




- Optimize AI applications for accuracy, latency, scalability, cost, and reliability.
- Implement techniques to reduce hallucinations and improve RAG response quality.
- Integrate AI solutions with enterprise applications, APIs, databases, and cloud platforms.
- Containerize and deploy AI applications using Docker and cloud platforms such as AWS/Azure/GCP.
- Collaborate with product, data science, engineering, and business teams to convert use cases into production-ready AI solutions.

Mandatory Skills
- Strong Python programming
- Hands-on experience with LLMs / Generative AI
- Strong understanding of RAG
- Experience building AI Agents / Agentic AI workflows
- LangChain and/or LangGraph
- Prompt engineering and LLM application development
- Experience with Vector Databases
- Embeddings, semantic search, similarity search and retrieval techniques
- REST APIs / FastAPI
- Valuable understanding of NLP and Transformer architec

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