Sairam Krishnan

Engineering Leader · AI/ML Platforms

Start with architecture.Finish with ROI.

I lead teams that turn ambitious technical systems into adopted, measurable outcomes. Currently building AI/ML platforms at Apple.

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Solid. Modular. Scalable.

Define the model, boundaries, and failure modes before scale turns ambiguity into cost.

A canonical media graph unified more than fifteen sources.

A media knowledge system moving from sources to product outcomesSource data passes through normalization and event-driven ingestion into a canonical knowledge graph used by search, recommendations, and discovery.SearchRecommendationsDiscovery

Apple

A shared knowledge layer for media

Search, recommendations, and discovery depended on fragmented metadata and inconsistent models.

My responsibility

Led architecture and cross-functional delivery of a canonical media knowledge graph.

Apple media knowledge graph architectureMore than fifteen media sources converge through normalization and event-driven ingestion into a canonical knowledge graph that supports search, recommendations, and discovery.
Fifteen-plus inputs become one model, one operational path, and three product surfaces.

Key decisions

  1. Unified more than fifteen sources around one model
  2. Moved critical ingestion from batch toward event-driven processing
  3. Built quality and operational leverage into the platform

ScopeEight engineers across three continents, serving more than 100 million users.

40%better data quality

30%faster delivery

“Sairam has established himself as Trinity’s expert on our curation infrastructure.”
Apple manager review2025 performance review

The difficult part was not choosing a graph. It was creating one technical truth that teams in different disciplines and locations could build on together.

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Apple

A global launch with one operating rhythm

Apple Music Credits required coordinated delivery across teams, sites, and technical boundaries.

My responsibility

Directed the program from architecture alignment through global launch.

Four international sites converging on one global launchFour engineering sites connect through aligned ownership and dependencies to a coordinated Apple Music Credits release.Site 01Site 02Site 03Site 04Global launch
Four sites, one release path, and explicit operating ownership.

Key decisions

  1. Made ownership and dependencies visible
  2. Connected technical decisions to launch readiness
  3. Kept geographically distributed contributors aligned

ScopeA fifteen-engineer program across four international sites.

15 / 4engineers / international sites

ResultTurned a cross-site initiative into a coordinated global release.

The architecture mattered, but the launch depended on a shared operating rhythm: visible dependencies, explicit ownership, and decisions made at the level where they could move.

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Amazon Alexa

A technical choice with P&L consequence

Large-scale data mining costs and model performance constrained the value of Alexa Engine analysis.

My responsibility

Developed a batching and clustering approach that changed both economics and model quality.

Alexa workloads reorganized into efficient clustersA large data workload is batched and clustered, producing lower annual cost and higher model accuracy.Raw workloadBatchClusterCostAccuracy
One technical intervention changed both unit economics and model performance.

Key decisions

  1. Reframed the workload around cost and throughput
  2. Applied clustering where it improved both efficiency and signal
  3. Measured the business outcome alongside model performance

ScopeA production Alexa data-mining system at hyperscale.

$40Msaved per year

40%better model accuracy

ResultSaved $40 million per year and improved model accuracy by 40%.

The breakthrough was treating cost and model quality as one engineering problem. Reframing the workload improved the economics and the intelligence of the system at the same time.

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Independent work

Systems tested in public.

Four products explore trustworthy AI, knowledge infrastructure, and disciplined adoption.

  1. 01Ondoway
  2. 02Persuaider
  3. 03Lore / DependIQ
  4. 04Crucible

Ideas

Arguments built from the work.

Research and teaching on AI investment, engineering apprenticeship, and autonomous systems.

  1. 01Who Trains the Next Senior?
  2. 02A Multi-Layer Framework for Evaluating the ROI of AI Projects
  3. 03Drone-Megaddon