Key Responsibilities

• Build and deploy LLM-based GenAI applications end-to-end
• Work with multiple LLMs (OpenAI/Azure OpenAI, Gemini, Claude, LLaMA, Mistral)
• Design and implement agentic and multi-agent systems using MCP and A2A
• Develop RAG pipelines and document intelligence solutions
• Use modern GenAI frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Google ADK)
• Build AI backend services using FastAPI and Flask
• Develop and integrate chat UIs, copilots, and dashboards with AI APIs
• Productionize, deploy, and monitor AI systems on cloud platforms
• Ensure performance, security, scalability, and Responsible AI compliance

Required Skills

• Strong Python programming with FastAPI and Flask
• Solid understanding of ML, Deep Learning, NLP, and GenAI concepts
• Hands-on experience with LLMs, embeddings, and prompt engineering
• Experience designing agentic systems with MCP / A2A
• Expertise in RAG architectures and vector databases (FAISS, Pinecone, Weaviate, Chroma, Milvus)
• Working knowledge of front-end technologies: HTML, CSS, JavaScript, Stremlit; React preferred
• API design and integration (REST / WebSockets)
• Strong deployment & MLOps experience: Azure / AWS / GCP, Docker, Kubernetes, CI/CD, MLflow / Azure ML / SageMaker / Vertex AI

Good to Have

• Fine-tuning open-source LLMs (LoRA, PEFT, Hugging Face)
• Experience with autonomous agents and async workflows
• Exposure to RPA + Agentic AI integration
• Knowledge of AI governance, monitoring, and security