AI Engineering Leader · Engineering Manager, AI Transformation & Platform Engineering

I build AI systems, and I check whether the numbers behind them are real.

I build AI systems, lead an engineering function, and write up what the numbers actually said — including when they said I was wrong.

Experience Start with one essay

sangeethcloud@gmail.com · LinkedIn · Chennai, India · open to remote

What I build

I build assistants that answer questions from a company's own documentation, the pipelines that keep that knowledge current, and the systems that say whether any of it actually works.

What I buildThe AI Knowledge Assistant chunks documentation through the Context Governance Framework into a knowledge store, while an agent-loop assistant searches that store and greps or reads source code to answer an engineer's question, scored by an evaluation harness. The Work Progress Intelligence Platform extracts structured updates from conversational AI standups, syncs two ways with the work-tracking system, feeds role-shaped dashboards, and answers questions through a chat assistant. The Engineering Capacity & Demand System models demand and effective capacity into a backlog breakpoint that feeds a leadership briefing. The AI-Native SDLC multi-agent engineering workflow, a design, has an orchestrator coordinate specialised agents while a human approves contracts, foundational changes and final closure. Lines join only parts that feed each other.AI Knowledge AssistantDocumentationchunkedContext GovernanceFrameworkcandidate → gate →a person promotes(the design)keeps it currentKnowledge storeSource codeEngineer'squestionAssistant: agent loopthe model chooses tosearch, grep or readsearchgrep, readSource-groundedanswerEvaluation harnesscorrectness · completeness ·faithfulnessscores its answersWork Progress Intelligence PlatformConversational AIstandupsextract structuredupdatesPlatformWork-trackingsystemtwo-way syncDashboards shaped toeach roleChat assistantroutes each question to a toolreads through toolsEngineering Capacity &Demand SystemDemandEffectivecapacity, notheadcountBacklog breakpoint, asstaffing changesLeadership briefingfeedsAI-Native SDLC — Multi-Agent Engineering Workflow(designed)OrchestratorcoordinatesSpecialised agentsproduct · architecture ·development · QA ·security · DevOpsA human approvescontracts · foundationalchanges · final closureasks for approval What I buildThe AI Knowledge Assistant chunks documentation through the Context Governance Framework into a knowledge store, while an agent-loop assistant searches that store and greps or reads source code to answer an engineer's question, scored by an evaluation harness. The Work Progress Intelligence Platform extracts structured updates from conversational AI standups, syncs two ways with the work-tracking system, feeds role-shaped dashboards, and answers questions through a chat assistant. The Engineering Capacity & Demand System models demand and effective capacity into a backlog breakpoint that feeds a leadership briefing. The AI-Native SDLC multi-agent engineering workflow, a design, has an orchestrator coordinate specialised agents while a human approves contracts, foundational changes and final closure. Lines join only parts that feed each other.AI Knowledge AssistantDocumentationchunkedContext GovernanceFrameworkcandidate → gate →a person promotes(the design)keeps it currentKnowledge storesearchEngineer'squestionAssistant: agent loopthe model chooses tosearch, grep or readgrep, readSource codeSource-grounded answerscores its answersEvaluation harnesscorrectness · completeness ·faithfulnessWork ProgressIntelligence PlatformConversational AIstandupsextract structuredupdatesPlatformtwo-waysyncWork-trackingsystemDashboards shaped toeach rolereads through toolsChat assistantroutes each question to a toolEngineering Capacity &Demand SystemDemandEffectivecapacity, notheadcountBacklog breakpoint, asstaffing changesfeedsLeadership briefingAI-Native SDLC — Multi-Agent Engineering Workflow(designed)OrchestratorcoordinatesSpecialised agentsproduct · architecture ·development · QA ·security · DevOpsasks for approvalA human approvescontracts · foundationalchanges · final closure
What I build, and what feeds what. The Context Governance Framework keeps the AI Knowledge Assistant's knowledge current, and an evaluation harness scores its answers. The Work Progress Intelligence Platform syncs two ways with the work-tracking system. The capacity system feeds a leadership briefing, and the agent-based delivery workflow is a design. Lines join only parts that feed each other. In my own system, not every part of the governance lifecycle was enforced.

AI Knowledge Assistant

An internal assistant giving engineers source-grounded answers about a large multi-service platform.

Context Governance Framework

A lifecycle for the knowledge feeding an LLM: candidate-only builds meant to keep automated jobs off live, a contract gate that blocks silent shrinkage and lost facts (answer scoring advisory), manual reversible promotion, pinned versions, one-click rollback.

Work Progress Intelligence Platform

Conversational AI standups that extract structured updates, two-way sync with the work-tracking system, and dashboards shaped to each role.

Engineering Capacity & Demand System

Models demand against effective capacity rather than headcount and projects the backlog breakpoint as staffing changes, feeding a leadership briefing.

AI-Native SDLC — Multi-Agent Engineering Workflow

Designed a software-delivery workflow built from specialised agents, coordinated by contract-driven development: architecture contracts are frozen before implementation starts, changes pass through structured governance, and a human approves contracts, foundational changes and final closure. Paired with review expectations for AI-generated code.

Experience →

Decks

One for an engineering audience, one for the people deciding whether to fund the work. Both include the parts that didn't go well.

Leadership · 11 slides · ~20 min Engineers transformed. Engineering didn't. Individual engineers write code far faster with AI. No organisational number moved that anyone could attribute to it. Technical · 12 slides · ~20 min Governing the information your AI reads Two incidents, a framework, and the uncomfortable discovery that one layer of it — the authority ranking — was built, wired, and doing nothing at all.
All decks →

Six things I keep running into

Recurring problems across the systems I've built. I'd rather show the pattern and what it cost than list technologies.

Leading an engineering function

Capacity as a modelTurning "the team feels underwater" into a projection leadership could plan against. Then scoring my own forecast against what happened, finding it too pessimistic, and then finding my first correction had only scored part of it. Soon
Seeing how an engineering org worksBuilding the system that tracks how work actually flows. Raising an assistant's high-confidence answers from 81 to more than 90 out of 100 (its own confidence label) was not a data problem: the data had been there all along, and the failures were routing gaps and tool bugs. Soon

Building AI systems

Governing what the AI readsTreating the information you feed a model the way you treat code — versioned, checked, approved by a person, reversible. Including the uncomfortable part: the authority ranking we built for it was wired in everywhere and did nothing at all. Read →
A release pipeline for knowledge, as designedCandidate, versioned, never what users see, checked, into the gate, which blocks silent shrinkage and blocks lost facts, with answer scoring beside the gate as advisory; a check fails leads to nothing promoted; checked leads to a person promotes, manual, reversible, beside a pinned known-good version for one-click rollback; then production, watched, staleness caught, gaps healed; healed context goes back through the same gate. The design. In my own system, not every part of it was enforced.The design. In my own system, not every part of it was enforced.Answer scoringbeside the gate ·advisoryCandidateversioned · neverwhat users seecheckedGateblocks silentshrinkageblocks lost factscheckedA personpromotesmanual ·reversibleProduction,watchedstaleness caught ·gaps healeda check failsNothingpromotedone-click rollbackPinnedknown-goodversionhealed context goes back through the same gate A release pipeline for knowledge, as designedCandidate, versioned, never what users see, checked, into the gate, which blocks silent shrinkage and blocks lost facts, with answer scoring beside the gate as advisory; a check fails leads to nothing promoted; checked leads to a person promotes, manual, reversible, beside a pinned known-good version for one-click rollback; then production, watched, staleness caught, gaps healed; healed context goes back through the same gate. The design. In my own system, not every part of it was enforced.The design. In my own system,not every part of it was enforced.Candidateversioned · never what users seecheckedGateblocks silentshrinkageblocks lost factsAnswerscoringbeside the gate ·advisorya check failsNothingpromotedcheckedA personpromotesmanual ·reversiblePinnedknown-goodversionone-clickrollbackProduction, watchedstaleness caught · gaps healedhealed context goes back throughthe same gate
Treating the information a model reads the way you treat code: a versioned candidate, a gate that blocks silent shrinkage and lost facts while answer scoring runs beside it as advice, a person who promotes, a pinned known-good version for one-click rollback, and healed context sent back through the same gate. That's the design; in my own system, not every part of it was enforced.
Search that answers questionsA fix for "list all of them" that worked perfectly and quietly degraded questions that wanted an explanation. A slow step whose timings had been recorded on every request all along — three-quarters of retrieval time, and the reason an eight-way parallel check ran one request at a time. Read → When the measurement liesA test that flattered me for months — first because it was small and skewed toward one kind of question, then because part of it had no answer key to score against. A forecast I checked two months later: the bug projection wrong by more than twice over, the broader one by about 30%. Read → Failures that went around the gateA job that destroyed most of a live index, then a different job did the same months later, after the safeguards existed. Read →

What production actually taught me

Not tutorials. The things I got wrong first, and the measurements that told me so.

September 2026 · 6 min · no background needed The five ways context fails When an AI feature gives a wrong answer, the instinct is to blame the model. Sometimes the model is the problem; often it's the information you handed it. Five distinct failures, five different fixes, and stories from the ones I got wrong myself. September 2026 · 3 min The harness told me my AI was worse than I thought I built a system to measure whether the assistant I'd shipped was any good. It told me it wasn't — and that was the most useful thing that happened all quarter. September 2026 · 4 min A gate that isn't wired into the robot is decoration An unattended job destroyed most of a live search index, twenty minutes after a healthy version went live. The safety checks existed — they just weren't on the path the robot took. Then it happened a second time.
All writing →

Most teams tune their systems against a ruler nobody has checked. The outputs are the part everyone worries about — but the measurement drifts too, and it drifts quietly, because nothing measures the measurement.

— the habit behind most of what's here
The lab — not built yet

Three demos, planned

These don't exist yet. They're listed because I'd rather show what I plan to build than claim it's finished. Each becomes a working link the day it runs.

The release gate
Run an automated update against a small knowledge base and watch the safety checks catch a bad one before it reaches users. Then override them on purpose, watch it break, and put it back with one click.
The unreliable ruler
Score the same forty questions twice, with nothing changed in between, and get two different results. See which ones flipped. Then run it repeatedly and watch a trustworthy number appear out of the noise.
Question answering, opened up
Ask questions of a public documentation set and get answers with sources you can click. See which passages were found, which were thrown away, and how long each step took. Ask something it can't know, and watch it say so rather than invent.