Technical Program Manager · AI Systems Builder
Scaling IdeasInto AI Systems.
I lead programs at Microsoft Azure scale — turning strategy into governed developer platforms, responsible AI adoption, and measurable gains for global engineering teams, without exposing confidential systems.
Latest research prototype: DEXter MCP — a personal learning project exploring a privacy-aware, phone-first voice system in controlled execution, turning intent into governed actions across research and everyday tools.
Case Studies.
Enterprise AI, developer platforms, technical programs, and scalable operating systems — grounded in measurable, public outcomes.
Enterprise Developer Platform · Azure Scale
Unified Developer Platform
Co-led a multi-team program that brought two AI-enabled developer experiences into one governed portal direction for a 12,000+ engineer audience.
Enterprise AI Automation · Azure Scale
Safety-First AI Automation
Designed a governed launch playbook for Copilot Studio automation using approval gates, behavior checks, A/B validation, and phased releases.
DEXter
MCP_SUITE
v1.0 · Governed Execution Layer
A phone-first MCP layer that converts spoken intent into governed actions. It coordinates everyday tools, filters sensitive identifiers before model calls, and keeps raw archives outside the public assistant.
// Initialize MCP v1.0 Handshake
const mcp = new DEXterMCP({ version: '1.0' });
mcp.connect({ backend: 'dexter-rise' });
mcp.applySafety({ alignment: 'Constitutional' });
✓ Protocol handshake complete
// Engineering in progress...
▋Writing Archive.
Build notes, architecture decisions, and technical deep dives.
Direct Voice API Prototyping with Google Live
An independent builder's exploration of low-latency conversational agents: direct WebSocket audio, turn-taking, and lightweight state steering.
Local Token Dynamics & Context Optimization
An engineering look at tokenization quirks: why byte-pair encoding boundaries break regex filters, how to budget local vs. cloud context windows, and structural chunking tradeoffs in retrieval-augmented pipelines.
The Genesis of Dexter
A Complementary Local-First Architecture for Targeted Data Safety
An independent technical exploration of a local-first MCP runtime designed to minimize unnecessary cloud data exposure, reduce external dependencies, and support privacy-sensitive AI workloads.
Local Processing
Privacy-aware runtime
Native Speed
Memory-mapped exec
Autonomous Ops
No cloud dependency
Local LLM
Open-weights on-device
Get in Touch.
Always open to connecting with fellow engineers, researchers, and technical program leaders to discuss the future of AI and developer platforms.