About
I operate at the intersection of engineering, product, and applied AI — translating executive intent into production systems, and elevating ground-truth technical reality back into strategic decision-making. The work lives in both rooms: the architecture review and the strategy table.
My focus is scaling Agentic AI platforms and multi-agent systems that move enterprises beyond isolated pilots into durable production capability. The leverage isn't in any single use case — it's in the substrate beneath them. That conviction shapes the three strategic areas where I'm doing my most forward-leaning work:
1. Fine-tuning to develop Small Language Models (SLMs)
Frontier models are powerful but general. The next edge is specialization: fine-tuned SLMs that match frontier quality on narrow, high-volume enterprise tasks — at a fraction of the cost and latency, deployable close to the data, and governable by design.
2. Unstructured data management
Most enterprise knowledge is locked in documents, messages, and media — invisible to AI until it's engineered into a retrievable asset. I build the document understanding and knowledge base pipelines that make it one, treating retrieval quality — relevance, grounding, freshness — as a first-class, measurable concern.
3. Agentic platform engineering
Agents don't fail for lack of intelligence; they fail for lack of infrastructure. I architect the common services that let hundreds of use cases share one governed foundation:
- Context engineering harness — assembling, compressing, and routing the right context into agents, reliably and at scale
- Evaluation harness — making agent quality measurable before production and provable in it
- AI observability harness — tracing, reliability, cost, and impact made visible end to end
This is frontier territory — the patterns are being defined in real time, and I'm building them in production, not reading about them.
The throughline is platform thinking: cost discipline, governance, extensibility, and measurable impact engineered into the foundation rather than retrofitted onto it.
What I'm known for: converting complex technical challenges into production-ready platforms, aligning stakeholders across business and engineering, and shipping systems that balance time-to-market, extensibility, and enterprise governance.
Current focus
Enterprise GenAI Common Services
Reusable substrate: retrieval, tool execution, orchestration, observability — so 200+ use cases ship faster, with governance built in.
Multi-agent system design
When agents should disagree, eval harnesses as architecture, dissent thresholds, blameless human-in-the-loop.
AI in regulated workflows
Prior authorization, clinical decision support, PHI-aware patterns at population scale.