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
Robust 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
Good understanding of NLP and Transformer architectures
Experience with at least one major LL

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