Leading AI in the SDLC

The through-line

I lead multi-location engineering for large-scale learning platforms and still work hands-on across architecture, AI workflows, and quality. The goal is consistent: make Human-AI collaboration disciplined enough to measure, not demo theater. For a senior engineering leader, the real question is not "do you use AI" - it is how you govern it, keep quality high, and prove the outcomes.

How I lead

Engineering leadership here means building the org, not only the model. I grew a multi-location team from 15 to 22 to about ~30 engineers across Noida and Mysore, with 9 direct reports. Scope covers hiring and mentoring, delivery governance, stakeholder management, and roadmap ownership for a multi-product portfolio. US executive engagement sits in the work history where it is accurate. The same player-coach bar applies to AI adoption: I still ship architecture and quality gates while the team scales.

AISprints and Agentic SDLC

AISprints - a Human-AI collaboration approach built around technical PRDs, guardrails, and micro-sprints. This is where my structured AI-assisted delivery work began, with measured pilot outcomes.

Agentic SDLC - an orchestration approach for AI-first delivery: context gates, multi-agent swarms, eval-driven quality gates, and the Delegate / Review / Own loop. The fuller model for teams moving beyond autocomplete-style assistance. AWS publishes a related named method as AI-DLC; that is a vendor methodology, not a claim that this work is an AWS implementation.

Self-hosted LLM governance

Privacy-first Mistral / Codestral tooling for coding, test generation, and documentation, used by about ~150 internal engineers. The priority is governance, security, and real adoption over vendor lock-in or demo theater.

Agentic product quality and ML

Production machine learning in EdTech: student retention and course-risk models at about ~91% precision on millions of learning events, alongside agentic remediation patterns kept inside strict guardrails.

Measured outcomes

On pilot feature releases: about ~50% defect reduction and about ~30% engineer time saved where GenAI-assisted flows applied. These are pilot results, labeled as pilots. See pilot results for the verified numbers in context.

How teams adopt it

A phased, lower-risk rollout: context stabilization, pilot micro-sprints, automated eval integration, then standardization. A public kit is planned under Build.

Who it is for

Engineering leaders adopting AI in the SDLC who still care about quality, ownership, and measurable delivery - especially in regulated or high-scale product organizations.