Key Responsibilities

• Design, develop, and deploy AI agents and GenAI applications using Python and modern AI frameworks
• Build autonomous and multi-agent systems leveraging LLMs and prompt engineering techniques
• Implement Retrieval-Augmented Generation (RAG) using vector databases and enterprise knowledge sources
• Integrate AI agents with REST APIs, databases, enterprise applications, and third-party services
• Develop scalable backend APIs using FastAPI or Flask
• Optimize prompts, agent workflows, and AI model performance for accuracy and reliability
• Package and deploy AI applications using Docker and cloud platforms (AWS, Azure, or GCP)
• Develop, test, and maintain AI applications using Git, PyTest, and CI/CD pipelines
• Collaborate with AI Architects, Data Engineers, and Product teams in Agile environments
• Implement AI governance, security, privacy, and responsible AI practices

Requirements

• Strong programming experience in Python
• Hands-on experience with OpenAI, Azure OpenAI, or other LLM APIs
• Experience with LangChain, LangGraph, CrewAI, AutoGen, or similar agent frameworks
• Strong knowledge of Prompt Engineering and Retrieval-Augmented Generation (RAG)
• Experience with Vector Databases such as Pinecone, Weaviate, ChromaDB, or FAISS
• Experience developing REST APIs using FastAPI or Flask
• Knowledge of SQL, NoSQL, JSON, and XML
• Experience with Docker, Git, CI/CD, and cloud platforms (AWS, Azure, or GCP)
• Familiarity with PyTorch or TensorFlow
• Experience with unit testing using PyTest and API testing using Postman

Skills Required