Put AI to work across the whole SDLC — requirements, architecture, code, tests, security, docs and deployment.
Not sure yet? Join the free 90-min webinar
Most teams have adopted AI at exactly one point in the lifecycle: a developer typing in an editor with an autocomplete plugin. That is the smallest available win.
The larger one is everything around it — understanding a codebase nobody remembers writing, generating tests that actually cover the risk, doing impact analysis before a change, keeping documentation true, reviewing for security, planning a migration. These are the parts of engineering that consume the most time and where AI is least used.
This workshop is for experienced engineers who want to apply AI across the lifecycle deliberately, and to know where it helps, where it is a liability, and how to tell the difference. You will build an AI Software Engineer: a system that reads a real repository and answers real engineering questions about it.
Requirement / Ticket
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Analyse Architect Generate Test Secure Document
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Repository Index (AST + symbols + embeddings)
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Impact analysis -> Change plan -> Human review
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Deployment
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, API systems, code intelligence, application modernisation and AI testing and validation.
This track comes directly from that work: applying AI to the software lifecycle itself. It covers practical engineering concepts and general industry knowledge, and does not expose proprietary client or employer intellectual property.
That one is about building AI products for your users. This one is about applying AI to how your team builds software. Different audience, different outcome — some engineers do both.
No. Those tools appear, but the workshop is tool-agnostic and focuses on engineering workflows that outlast any single vendor.
No, but you do need real software engineering experience. This assumes you have worked on non-trivial systems.
In the public batch we use open-source repositories. For in-house delivery we can work on your codebase under NDA — mention it in the enquiry.
No. The consistent finding is the opposite: AI raises the value of engineers who understand systems deeply, and it is unreliable in the hands of those who do not.
Day 3 covers this directly — what must never reach a third-party model, and how to structure workflows so it does not.
Yes — a Certificate of Completion with a verifiable link.
Yes, this is the track most often delivered in-house. Use the enquiry form and mention your team size.
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3 days · 4 hrs/day · 12 hours live · Live Online · ₹4,999