About the role Job Title: Python Developer - Agentic AI / LLM / RAG Engineer Job Level: JL3 / JL4 (Based on Experience) 1-6 Yrs Location: Bangalore/ Pune Job Summary We are seeking a skilled Python Developer with expertise in Agentic AI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) to design, develop, and deploy intelligent AI solutions. The candidate will be responsible for building AI agents, integrating LLM-powered applications, implementing RAG architectures, and optimizing AI workflows for enterprise use cases. Responsibilities Key Responsibilities Develop and maintain AI applications using Python. Design and implement Agentic AI systems using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar. Build and optimize RAG pipelines using vector databases and retrieval frameworks. Integrate and fine-tune Large Language Models (LLMs) including OpenAI, Azure OpenAI, Llama, Claude, Gemini, or similar. Develop APIs and microservices for AI applications. Implement prompt engineering, workflow orchestration, and AI agent collaboration patterns. Create scalable solutions using cloud platforms such as Azure, AWS, or GCP. Monitor, evaluate, and improve AI model performance, accuracy, and response quality. Collaborate with business and technology teams to translate requirements into AI-driven solutions. Technical requirements Required Skills Strong programming experience in Python. Hands-on experience with LLMs, Generative AI, Agentic AI, and RAG architectures. Experience with LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or equivalent frameworks. Knowledge of vector databases such as Pinecone, ChromaDB, Weaviate, FAISS, or Azure AI Search. Experience with REST APIs, FastAPI, Flask, or Django. Understanding of embeddings, semantic search, prompt engineering, and model evaluation. Experience with Git, CI/CD, and software development best practices. Knowledge of cloud AI services (Azure OpenAI preferred). Preferred Skills Experience with multi-agent architectures and AI orchestration. Knowledge of MLOps, model deployment, and containerization (Docker/Kubernetes). Exposure to NLP, machine learning, and deep learning concepts. Experience with enterprise AI governance, security, and responsible AI practices. Preferred skills Technology->AI-AI Engineering->AgentOps,Technology->AI-AI Engineering->LLMOps,Technology->AI-Generative AI->Generative AI - Basic->chains,Technology->AI-Machine Learning->Responsible AI,Technology->Programming Languages - OS->Open Systems (Unix/Linux)->Python,Technology->Programming Languages - OS->Python - Open Source->Python Education Bachelor of Engineering