AI Product Engineer
I Design, Build & Ship Your AI Product End to End
Full-stack AI products on the modern stack: TypeScript, Next.js, Supabase, and the Vercel AI SDK. I lead the product, architecture, and critical engineering decisions from scope through launch, and I remain accountable for what ships.
Trusted by Industry Leaders and Innovators
Need an AI Product Shipped, Not a Strategy Deck?
I work with founders and product teams who need:
- •A working AI/RAG product in users' hands in weeks, not a six-month roadmap
- •A full-stack build on the modern stack (Next.js, Supabase, Vercel AI SDK), led coherently from front to back
- •AI features that actually work in production: real retrieval, real grounding, no hallucinated demos
- •A senior builder who ships the code, not a consultant who draws the diagram
How It Works
A predictable, low-friction path from first conversation to a shipped AI product — and a clean hand-off when the work is done.
Intro Call
30 min
We talk about what you're building and whether I'm the right fit. No pitch, a real product conversation.
Scoping
Week 1
I produce a written proposal with scope, outcomes, timeline, and stack. You know exactly what you're getting, and what "done" means, before we start.
Build & Ship
Weeks (sprint) or ongoing (retainer)
I lead the build across frontend, backend, AI pipeline, and deployment. You get senior judgment at every layer.
Handoff
Closeout
A deployed product, clean docs, and, if you're hiring a team, code your engineers can maintain.
Principal-Led. Accountable End to End.
I scope the product, establish the architecture, and stay directly involved in the work that determines whether it succeeds. You always know who owns the outcome.
When a build needs additional delivery capacity, I add it deliberately and transparently while retaining technical and product accountability. No hidden handoff and no loss of senior oversight.
Signature Specialty
AI/RAG Products That Actually Work.
I take AI products beyond the prototype: ingestion, retrieval, grounded outputs, security, observability, and a maintainable application layer built around the real user workflow.
The goal is useful software, not an AI demo: validate the hard assumptions early, then build the system that can earn a place in production.
Engineering Outcomes at Enterprise Scale
Measurable results from production systems I personally designed, built, and shipped across Fortune 500 and private-equity-backed environments.
Connected building devices served by the event-driven, serverless TypeScript platform I built at Carrier (Abound BMS) — IoT Core, Lambda, DynamoDB/Timestream.
Uptime on the mission-critical, multi-region SaaS platform I architected and shipped for Siemens.
Infrastructure cost reduction I delivered through FinOps and architecture discipline on an enterprise SaaS platform.
Faster deployment cycles from the CI/CD pipelines I built and redesigned — GitHub Actions, blue-green releases, automated Vitest/Playwright gates.
Servers migrated to AWS at Atlas Air on the multi-account landing zone and migration tooling I designed and built (AWS CDK, Terraform).
Detection and response times from the security guardrails and observability I implemented across production environments (Security Hub, GuardDuty, CloudWatch/OpenSearch).
Insights for Builders and Technical Leaders
Practical thinking on product engineering, AI systems, and the decisions that turn a promising prototype into dependable software.
Ready to ship your AI product?
Let's talk. 30 minutes. Tell me what you're building: your idea, your timeline, your stack, and I'll tell you honestly whether I'm the right builder for it.