Python, Gen AI

Role OverviewWe are looking for a skilled Python Full Stack / Agentic AI Engineer to design, develop, and deploy AI-powered applications and intelligent agentic workflows. The ideal candidate should have strong expertise in Python, FastAPI, LLMs, RAG, LangChain/LangGraph, and modern full-stack development.You will work on building scalable backend services, integrating Large Language Models, developing AI agents, implementing Retrieval-Augmented Generation (RAG) pipelines, and creating production-ready AI applications.Key ResponsibilitiesDesign and develop scalable backend applications using Python and FastAPI.Build and deploy Agentic AI solutions using LLMs and agent frameworks.Develop multi-step and multi-agent workflows using LangChain and LangGraph.Design and implement RAG (Retrieval-Augmented Generation) pipelines.Integrate LLMs such as OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source models.Develop prompt engineering strategies and structured LLM workflows.Work with vector databases and embedding models for semantic search and knowledge retrieval.Build APIs and microservices for AI-powered applications.Integrate AI services with databases, third-party APIs, and enterprise systems.Develop conversation memory, tool calling, function calling, and agent orchestration capabilities.Implement evaluation, monitoring, logging, guardrails, and error handling for AI applications.Optimize applications for performance, scalability, reliability, and cost.Collaborate with product managers, frontend developers, data engineers, and other stakeholders.Write clean, maintainable, well-tested, and production-ready code.Participate in architecture discussions, code reviews, testing, and deployment activities.Required SkillsProgramming & BackendStrong proficiency in Python.Hands-on experience with FastAPI, REST APIs, and backend development.Strong understanding of asynchronous programming, API design, authentication, and middleware.Experience with SQL/NoSQL databases.Generative AI / Agentic AIStrong understanding of LLMs and Generative AI.Hands-on experience building AI Agents / Agentic AI applications.Experience with LangChain and/or LangGraph.Knowledge of agent orchestration, tool calling, function calling, memory, and workflow management.Strong understanding of prompt engineering.RAGExperience designing and implementing RAG architectures.Knowledge of document ingestion, chunking, embeddings, vector search, retrieval, reranking, and response generation.Experience with vector databases such as FAISS, Chroma, Pinecone, Weaviate, Qdrant, or similar.LLM & AI IntegrationExperience integrating commercial or open-source LLMs.Understanding of embeddings, context windows, temperature, token usage, and model selection.Experience with structured outputs and LLM-based workflows.Familiarity with LLM evaluation and observability is a plus.Full StackWorking knowledge of HTML, CSS, JavaScript/TypeScript.Experience with React.js or similar frontend frameworks is preferred.Ability to integrate frontend applications with Python/FastAPI services.