PROJECTS
Work That Moves Enterprises.
Selected case studies from 15+ years of leading AI and data initiatives — from governing AI risk to operating data at extreme scale.
Each engagement below is framed the way technology leaders evaluate it: the business problem, the approach, and the outcome.
01
AI Governance in Practice: Implementing the NIST AI Risk Management Framework
AI Governance · NIST AI RMF · Enterprise AI Strategy
The Challenge: Enterprise customers were eager to adopt AI but had no structured way to evaluate risk — every use case was a leap of faith, and leadership couldn’t answer basic questions about safety, compliance, or accountability.
The Approach: I implemented the NIST AI Risk Management Framework for multiple enterprise customers, starting discovery with the fundamental question — why do you want AI, and for what outcome? From there: use-case risk profiling, mapping risks to the framework’s Govern, Map, Measure, and Manage functions, and standing up governance and guardrails before deployment, not after.
The Outcome: Customers moved from ad-hoc AI experimentation to governed, auditable AI programs — with a repeatable framework they could apply to every future use case.
02
From Factory Floor to AI: IoT Data Lake + RAG for Manufacturing
Data Lake · IoT · RAG · Predictive Maintenance
The Challenge: A manufacturing customer generated billions of rows of IoT data, but faulty devices and faulty production components were discovered too late — after downtime had already cost money.
The Approach: I designed and built a massive data lake ingesting billions of IoT rows, with a data engineering pipeline that continuously checks for faulty IoT devices and faulty manufacturing components and reports them. On top of the pipeline, I connected an LLM system using RAG — so instead of just an alert, engineers get a diagnosis of the fault plus possible solutions.
The Outcome: The operation shifted from reactive firefighting to predictive maintenance — faults surface early, with AI-generated guidance on what to do about them.
03
The Graviton Play: Cutting Compute Cost at Fleet Scale
Cloud Cost Optimization · Architecture Migration · FinOps
The Challenge: A massive compute estate running on Intel and Nvidia architectures carried a cost structure that didn’t need to exist — but migrating architectures at fleet scale is where most cost programs die.
The Approach: I led complete Intel/Nvidia-to-Graviton architecture migrations: estate assessment, workload compatibility analysis, migration waves with validation at each step, and performance verification to ensure nothing regressed.
The Outcome: Fleet-wide compute cost reduction with performance intact — the kind of structural cost win that compounds every billing cycle.
04
Agentic AI in Production, Not Just Demos
Agentic AI · Production AI · AI Architecture
The Challenge: Every enterprise has an agent demo. Almost none have agents in production — because reliability, evaluation, guardrails, and cost control separate toys from systems.
The Approach: I design and deploy production-grade agentic AI solutions: system architecture, agent orchestration patterns, evaluation harnesses, deployment guardrails, and operational monitoring — the unglamorous engineering that makes agents trustworthy.
The Outcome: Agentic AI systems running in production, serving real users — beyond the pilot stage where most efforts stall.
05
Migration Modernization at Enterprise Scale
Cloud Migration · Modernization · Transformation Programs
The Challenge: Large enterprises know they need to modernize, but massive application and data migrations stall on complexity — every workload is a special case, and there’s no repeatable motion.
The Approach: I led massive application and data migration programs and created a go-to-market offering for migration modernization — turning one-off migration heroics into a repeatable playbook with defined phases, tooling, and success criteria.
The Outcome: A repeatable modernization motion that moves enterprises to the cloud at scale, instead of one workload at a time.
06
Operating Data at Extreme Scale
Database Operations · Large-Scale Systems · Engineering Leadership
The Challenge: Most architects design for scale they’ve never operated. Data estates of tens of thousands of databases punish every shortcut — in reliability, cost, and team burnout.
The Approach: I have operated a relational database fleet of 17,000–18,000 databases alongside large-scale Hadoop environments. Previously, I led a 130-person engineering and operations organization managing 35,000 on-premises SQL Server and 20,000 Oracle databases — spanning architecture, operations, and team leadership.
The Outcome: Operational excellence at a scale few practitioners have ever touched — the foundation beneath every architecture claim on this page.
LET’S TALK
Have an AI or data transformation in mind?
Let’s talk about what your organization could achieve.
