Short, hands-on intensives. Pick the track that matches your depth, build a working AI system over 12 live hours, and keep the code on your GitHub.
Students who code confidently can take the deeper tracks; experienced engineers can start with the fundamentals. Every track is live, small-batch and project-based.
Build 3 real AI projects in 3 days — the fundamentals track, open to students and working developers alike.
View syllabusBuild and deploy a real AI application in 3 days — LLMs, RAG, agents, APIs and production architecture. Not prompts.
View syllabusPut AI to work across the whole SDLC — requirements, architecture, code, tests, security, docs and deployment.
View syllabusBuild a production-grade agentic system in 12 hours — RAG, knowledge graphs, agents, MCP, evaluation and production architecture. Not another chatbot course.
View syllabusTools change every few months; the engineering underneath doesn't.
Work out whether AI belongs in it at all, before writing a line of code.
Turn the problem into an architecture: LLM, RAG, workflow, tools or a full agent.
Live, hands-on, in your own repository, with the instructor reviewing as you go.
Evaluation, hallucination, cost, latency and deployment: the parts nobody puts in a demo.
AI Product Engineer · Generative AI, AI Systems & Software Architecture
Dr Ganesh works on practical engineering problems involving generative AI, LLM applications, AI agents, RAG systems, knowledge graphs, software architecture and code intelligence.
He teaches the way the work is actually done — starting from a problem, ending at a deployed system. This workshop covers practical engineering concepts and general industry knowledge, and does not expose proprietary client or employer intellectual property.
Delivered live online or on-site, scoped around your stack. Engineering teams get a version built around one of your real problems; campuses get the fundamentals track with project reviews.
If you're new to building with LLMs, start with the AI Developer Bootcamp. If you already code confidently and want architecture, RAG evaluation and deployment, take AI Product Engineering. AI-Powered Software Engineering is for teams applying AI across the whole SDLC.
Python familiarity helps. The tracks focus on engineering concepts and architecture that apply across stacks, and we send a setup guide before Day 1.
Yes, recordings are shared with participants after each day.
Yes, a Certificate of Completion with a verifiable link you can add to LinkedIn.
See our refund policy for cancellations and batch transfers.