Job Title : Generative AI / Agentic AI Engineer

Location : Pune

Experience : 5+Years

Job Description :

We are seeking a highly skilled Generative AI / Agentic AI Engineer to design, develop, and deploy enterprise-grade AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Multi-Agent Systems. The ideal candidate should have strong expertise in NLP, Prompt Engineering, Agentic AI frameworks, and production-scale AI application development.

Key Responsibilities :

- Design and develop Generative AI applications using LLMs and modern AI frameworks.

- Build and optimize RAG and GraphRAG solutions for enterprise knowledge retrieval.

- Develop Agentic AI workflows and Multi-Agent Systems using frameworks such as LangGraph, CrewAI, AutoGen, or similar.

- Create and optimize prompts, tool-calling workflows, and agent orchestration pipelines.

- Integrate LLMs with enterprise data sources, APIs, vector databases, and knowledge graphs.

- Develop scalable backend services and APIs using Python and FastAPI.

- Evaluate, monitor, and improve AI model performance, response quality, and reliability.

- Collaborate with cross-functional teams to translate business requirements into AI-powered solutions.

- Deploy and maintain AI applications on cloud platforms such as Azure, AWS, or GCP.

Mandatory Skills :

- 2+ years of hands-on experience in Generative AI / LLM development.

- 3+ years of experience in Natural Language Processing (NLP).

- Strong proficiency in Python.

- Hands-on experience with Large Language Models (LLMs).

- Strong experience with Prompt Engineering.

- Experience with RAG (Retrieval-Augmented Generation).

- Experience building Agentic AI workflows.

- Experience with Multi-Agent Systems.

- Hands-on experience with LangChain and LangGraph.

- Experience integrating AI solutions with vector databases and enterprise data sources.

Preferred Skills :

- GraphRAG, Knowledge Graphs, or Neo4j.

- CrewAI, AutoGen, Google ADK, or similar agent frameworks.

- MCP (Model Context Protocol).

- Vector Databases : Pinecone, ChromaDB, FAISS, Qdrant, Weaviate.

- OpenAI, Azure OpenAI, AWS Bedrock, Gemini.

- LangSmith, LangFuse, or AI observability tools.

- FastAPI, Docker, Kubernetes, CI/CD.

- Fine-tuning, LoRA, QLoRA.

Education :

- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.