1. Summary

Product leader with 17+ years in data-intensive B2B enterprise SaaS, now building agentic AI systems in regulated life sciences. I take LLM systems from prototype to production: agent architecture, retrieval design, evaluation harnesses, confidence-based routing, and the governance that lets a regulated customer actually switch automation on.

My view: in enterprise AI the differentiator is rarely the model. It is evaluation, grounding, and where you draw the line between automation and human review. That is the part I own.

1.1 Selected outcomes

• Took a multi-agent classification platform from concept to production in 16 weeks, reaching roughly 80% straight-through classification across ~100K customer records (Marbls)
• Designed the confidence model and decision routing (auto / assist / review) that made automation acceptable to compliance in a government price reporting context (Marbls)
• Built evaluation and regression harnesses — gold sets, adversarial cases, error taxonomy — and used them to tune thresholds before rollout rather than after (Marbls)
• Cut manual review effort by ~25% with LLM-assisted validation and anomaly detection, with human-in-the-loop guardrails and audit-ready rationales (IntegriChain)
• Improved cross-functional delivery alignment by 40% through sharper PRDs, acceptance criteria, and earlier engineering/QA involvement (IntegriChain)
• Drove a 15% adoption lift through UX improvements and structured enablement (IntegriChain)

2. Core Strengths

AI product systems — Agent architecture (single-agent with tools vs. orchestrated multi-agent), retrieval and RAG design, tool and context interfaces, prompt contracts, structured output

Reliability and evaluation — Gold sets and adversarial suites, regression gates, error taxonomies, calibrated confidence, abstention and risk-tiered routing, drift monitoring

Governance in regulated environments — Human-in-the-loop design, audit trails and explainability, policy constraints, build-vs-buy for AI components

Enterprise product craft — 0-to-1 delivery, roadmap and prioritization under compliance constraints, enterprise GTM and enablement, PM mentorship and team leadership

Domain depth — Government pricing, chargebacks, rebates, GTN, 340B/HRSA, GPO/IDN/PBM contracting, customer master data quality