Generative AI Trainer Cum AI Developer

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Key Responsibilities
Generative AI Development (50%)
Architect, build, and deploy production-ready Generative AI solutions using Python, LangChain/LlamaIndex, OpenAI API, Anthropic, Hugging Face, and open-source LLMs (Llama, Mistral).
Design and implement Retrieval-Augmented Generation (RAG) architecture using vector databases (Chroma, Pinecone, FAISS, Qdrant) for enterprise document intelligence and semantic search.
Build multi-agent AI systems, custom chatbots, and workflow automation using tools like CrewAI, AutoGen, and LangGraph.
Fine-tune, evaluate, and optimize open-source foundation models for domain-specific tasks and latency/cost efficiencies.
Integrate GenAI backend pipelines with RESTful APIs (FastAPI/Flask) and front-end application interfaces.
Technical Training Mentorship (50%)
Deliver hands-on, structured training modules on Generative AI concepts, Prompt Engineering, Fine-tuning, RAG architecture, Vector DBs, and AI Application Development.
Mentor trainees, students, and developers through live AI build projects, code reviews, and real-world debugging workflows.
Develop up-to-date curricula, practical lab exercises, coding benchmarks, and project blueprints reflecting current GenAI industry practices.
Conduct technical assessments, hackathons, and mock technical interviews to prepare candidates for corporate AI roles.