AI product & program management
I build AI products and run AI programs, and I know the systems underneath them.
8+ years delivering multimillion-dollar cloud and digital transformation programs in banking, insurance, and financial information — and, since 2025, running AI delivery directly. I designed LearningNemo, an internal 35-module agentic AI platform, delivered it as product owner for a team of AI coding agents, and founded Zemi Research on top of it. What I bring is knowing how to get real, defensible output out of AI, and the delivery discipline to ship it.
PMP · NVIDIA-Certified Professional — Agentic AI & Generative AI
01 — The work speaks
Built to be checked.
I'd rather show than tell — so I built one.
LearningNemo is an internal 35-module agentic platform I designed and delivered as product owner for a team of AI coding agents. It now runs Zemi Research, producing frontier medical research dossiers across 9 domains. I ran it as a governed program, not an experiment — and that is what it taught me: clear architecture, honest trade-offs, and relentless verification.
What it does
- A CEO agent that plans, decides, and coordinates a team of specialists
- A 6-node reasoning loop: plan → meta-critique → execute → reflect → synthesize → safety gate
- Long-term memory and production retrieval (RAG) over real sources
- An analyst panel that debates and verifies evidence before it ships
- Evaluation, safety guardrails, and a tamper-evident audit trail
- Multimodal generation, charts, and real-time voice
- Evaluation loops, with self-improvement training in integration testing
- A real-time operator dashboard, and a deep-research report pipeline
02 — What I do
AI strategy and roadmap
Deciding what to build and why — use-case selection, clear scope, honest trade-offs, and a realistic path from pilot to production.
AI program delivery and governance
Running the program that ships it: intake and prioritization, a stage-gated lifecycle, decision and change control, risk management, and executive reporting.
Enterprise-grade by default
Security, compliance, governance, and reliability built in — so AI output holds up to real scrutiny, real load, and a regulator.
03 — How I deliver
Execution discipline that ships.
Shipping enterprise AI takes more than models — it takes execution discipline. This is how I deliver systems that hold up:
Architecture as the single source of truth
One canonical specification defines every component and its contracts — so a large system stays coherent end to end.
A gated delivery lifecycle
Every unit of work moves through architecture → spec → build → review → merge, each stage gated by the next.
Verify everything
Explicit review discipline, a living lessons-learned registry, and a hard rule: no silent fallback — a quietly degraded result is a failure, not a pass.
Nothing changes silently
Decisions, changes, issues, and root-cause analyses are all documented and cross-referenced.
Tested and gated
>85% coverage enforced in CI, plus deep end-to-end testing — verified on real hardware before anything ships.
04 — Selected work
LearningNemo
learningnemo.com →A CEO-led agentic AI research swarm: a reasoning CEO agent plans, decomposes work across specialist agents, checks its own work, and produces a governed deliverable — built NVIDIA-tool-first as a reusable, pluggable platform.
It now runs Zemi Research: 24 decision-grade medical research dossiers across 9 domains, plus two pre-registered preprints published with permanent DOIs.
05 — Experience
Recent
Founder
Zemi Research (Reyes Financial, LLC)
Decision-grade medical research dossiers for drug developers, pharma corporate development, and life-science investors, produced by LearningNemo — the agentic AI platform I designed. Before release, a human-in-the-loop stage runs out of band: Claude Opus agents check each dossier against the research frontier and audit it, and I review the final output. I own the product, the platform, and delivery. My contribution is the system, the method, and its governance — not bench science.
- Defined the product, positioning, and pricing for a catalog of 24 decision-grade dossiers across 9 medical domains — oncology, immunology, gene & RNA medicine, cardiovascular, infectious disease & AMR, rare disease, digital health, neurology, and emerging med-tech.
- Defined the deliverable itself: a ~100-page report whose sections include evidence-maturity and comparative-platform matrices, a mechanistic deep dive, safety / failure-mode and CMC readiness, validation and falsification gates, and the regulatory-endpoint pathway.
- Its paired 30–45-sheet workbook is the proof layer — across those sheets sit a claim ledger, per-claim source-support verdicts, a hypotheses ledger (13+ tiered), an audit issues log, and live statistical power calculations a buyer can re-run against their own assumptions.
- Engineered the method that drives AI past consensus summaries into frontier findings: domain-scoped retrieval over primary literature, an evidence taxonomy forcing every claim to declare maturity, hypothesis tiering that separates the known from the genuinely novel, retraction checking, and an adversarial audit pass whose job is to break the draft. Weak drafts revise or are refused, never shipped.
- Made quality measurable: a separate AI auditor scores every dossier on 15 dimensions, and release requires 90%+ with zero must-fix failures; all 24 cleared it, 15 after first audits of 52–67%. An NYC medical research doctor reviewed a full dossier, and I built in their feedback.
- Proved the method transfers beyond the catalog: the same pipeline produced two pre-registered preprints with permanent DOIs and a trial-design-grade clinical trial blueprint built from simulation and published evidence — explicitly not wet-lab and not IND-filed.
LearningNemo — the agentic AI platform behind the product
35 modules, NVIDIA-tool-first. Concept to production, as product owner for a team of AI coding agents (Claude Code, OpenAI Codex, and agents in the Cursor IDE).
- Orchestration — a reasoning CEO agent (LangGraph) that plans, decides per task whether to solve directly or decompose, dynamically spawns specialist agents, and self-checks through a six-stage loop: plan → meta-critique → execute → reflect → synthesize → safety gate. Tool discovery and creation via NVIDIA NeMo Agent Toolkit (NAT); MCP support is in integration testing.
- Model fleet — chose what runs where: the orchestrator is Nemotron 3 Nano 30B A3B, an open-weight model served locally on NIM (vLLM) with role-based BF16 / FP8 precision routing; the API tier runs Kimi K2.6 (262K context, multimodal) for deep research, planning, and composition, with a Kimi-only failover chain and no silent fallback to a weaker model, Grok for generated imagery, and an LLM-judge layer that scores and gates deliverables before they ship.
- Drew the line where the model stops — a multimodal LLM cannot draw a bar exactly 0.030 high when the data says 0.030, so every quantitative figure (forest plots, Kaplan–Meier and ROC curves, calibration and delta plots) routes through a deterministic matplotlib renderer driven by a declarative chart spec the composer emits, with pinned fonts and a colour-blind-safe palette for reproducibility. Generative imagery is reserved for conceptual diagrams.
- Medical research grounding — the swarm retrieves against primary sources through PubMed / NCBI E-utilities, ClinicalTrials.gov, Europe PMC, OpenAlex, Crossref, and Unpaywall, so every citation resolves to a real, checkable identifier rather than a model recollection.
- Memory & retrieval — production RAG on NeMo Retriever + nv-ingest with nv-embedqa-e5-v5 embeddings in ChromaDB, stratified long-term memory and an episodic buffer, and NeMo Curator for corpus cleaning, dedup, and PII detection; multi-GPU coordination on PyTorch / CUDA.
- Safety & evaluation — NeMo Guardrails with custom validators, “Iron Dome” graduated-autonomy controls, a tamper-evident hash-chained audit log, and NeMo Evaluator with custom metrics and anomaly detection. Self-play and LoRA distillation are in integration testing, not yet in production.
- Multimodal & interface — VLM image and video analysis, generative multimodal output, real-time conversational voice (LiveKit + ASR NIM), and a NiceGUI real-time operator dashboard instrumented with Prometheus.
- Delivery discipline — architecture as single source of truth, a spec-gated Arch → Spec → Code → Review → Merge lifecycle, 100+ logged architecture decisions, 130+ controlled changes, >85% CI-enforced coverage across 400+ test suites, and a no-silent-fallback rule — validated end to end on live GPU hardware.
Independent Research & Portfolio Management
Reyes Financial, LLC
Researched and invested in digital assets from primary sources (blockchain fundamentals, token economics, macro). From mid-2024, turned the firm's research to AI: followed frontier developments, compared LLM capabilities, and tested AI platforms, the work that led to LearningNemo.
Foundation
Product and program management leadership in banking, insurance, and financial information — the execution discipline behind the AI work.
Cloud Sr. Program Manager
Northwestern Mutual
Owned intake, prioritization, and the delivery roadmap for the AWS cloud team; built the program board as the single source of status; led ceremonies and PI planning; reported to VP executives.
Public Cloud Program & Product Manager
TD Bank Group
Led a multimillion-dollar AWS/Azure program with a dedicated team of 12, where cloud security was the central concern; led the Microsoft Azure contract negotiation across Legal, Compliance, Privacy, Cyber Risk, and Audit (US + GDPR).
Lead Program Manager — Hybrid Cloud
McGraw Hill Financial
Delivered a multimillion-dollar hybrid-cloud self-service platform across five businesses (VMware vRealize, automation, vBlock), and ran steering committee meetings with leaders across all five.
Sr. Program / Sr. Project Manager
NYK Line · Port Authority of NY & NJ · Eastern Computer
Led data-center migration, consolidation, and virtualization programs — including a $17M NYK Line relocation to an IBM data center (with IBM/TCS) and the Port Authority's Jersey City-to-Newark server migration — then delivered migration, virtualization, and disaster-recovery consulting at Eastern Computer.
06 — Credentials
Certifications
Project Management Professional (PMP) and NVIDIA-Certified Professional — verified on Credly.
Previously certified: PMI-ACP · CCSP · CCSK · AWS Solutions Architect · ITIL
Education
- Master of Engineering, Electrical Engineering — Rensselaer Polytechnic Institute (RPI)
- B.S., Electrical Engineering — RPI
- Graduate coursework in business strategy & finance
07 — Contact
Let's talk about what you're building.
I'm open to AI Product Manager and AI Program Manager roles in New York, remote or hybrid.