German Engineer · 10 Years US Big Tech · Available for Engagements

I build the AI that does
the job after I leave.

Three to six months. Custom AI infrastructure. Systems that keep running and keep shipping when the engagement ends. Bringing Uber-scale engineering to Europe.

Fractional CTO / Staff Engineer / AI Infrastructure / DACH + EU
15+
Years in production systems
10M+
Requests/sec scaled at Uber
14x
Team built at HERE (2 to 14)
3-6mo
Typical engagement window

You're paying five-year salaries
for problems that take six months.

Most companies hire a full-time engineer to build something once, then keep them around to maintain it. There's a better model.

01

The Problem

Senior AI engineers command $300–500k in total compensation. You're hiring them to solve a specific architectural problem, then keeping them on indefinitely to justify the headcount.

02

The Solution

A focused 3–6 month engagement. I assess what you have, design the AI infrastructure you actually need, build it, deploy it, document every decision, and train your team to own it.

03

The Result

Systems that keep running. Runbooks your team can follow. AI agents handling work that used to require human hours. ROI that shows up in year one.


Results across scale, not just credentials.

PhD Physics, MBA, Staff Engineer at Uber. But what actually matters is what got shipped.

Uber — Staff Software Engineer

Navigation Platform at 1,400+ Nodes Per Data Center

Challenge
Off-route detection algorithms needed continuous improvement without risking regressions across Uber's largest service
Scope
Serving all Uber apps globally. Java core, on-device iOS/Android binaries, Python simulation infrastructure
Outcome: Built Python-based parallel simulation infrastructure for safe algorithm iteration. Developed on-device software for real-time cloud data ingestion. Managed GDPR-compliant data deletion across distributed databases across two clouds.
Python Java Distributed Systems GDPR Compliance Go
HERE Technologies — Engineering Manager

Autonomous Driving Capture Fleet from 2 to 14 Engineers

Challenge
Build a team and capture system capable of powering the next generation of HD mapping vehicles for autonomous driving
Scope
New generation HERE True capture vehicles, small-scale collection systems, full hardware-software integration
Outcome: Grew the Capture Systems Software team from 2 to 14 engineers. Designed and delivered the capture architecture that shipped in production vehicles. Built real-time and batch analytics for the global mapping fleet using Apache Storm.
Team Building Apache Storm Python Streaming Analytics Hardware-Software
Postmates / Uber — Senior Engineer II

Python 2 to 3 Migration Across a Full Monorepo

Challenge
Migrate the entire Postmates monorepo from Python 2 to Python 3 without breaking critical delivery infrastructure
Scope
Core infrastructure monorepo, distributed testing across Kubernetes pods, 200+ Python services at Uber
Outcome: Completed full Python 2-to-3 migration at Postmates. At Uber, scraped and analyzed the full dependency graph for all Python microservices, targeted the top 200 packages covering 90%+ of services, and drove the company-wide migration — including an overhaul of the open-source tchannel-python library.
Python Kubernetes Dependency Analysis Open Source
Westover Labs — Founder / CTO

AI Agent Team That Ships Code Around the Clock

Challenge
Build a solo-founder development operation that could produce team-level output across iOS, backend, and infrastructure simultaneously
Scope
Two App Store apps, two FastAPI inference backends, ONNX runtime serving, full CI/CD, self-hosted infrastructure
Outcome: Deployed a 9-persona AI agent team (Familiar) operating as a virtual engineering org — planning, coding, reviewing, and deploying continuously. Two iOS apps live on the App Store. ONNX inference backend serving real users. The system ships features independently.
Claude AI ONNX Runtime FastAPI React Native Ansible Cloudflare
Stack
Python FastAPI ONNX Runtime PyTorch React Native Ansible Cloudflare Tailscale PostgreSQL Kubernetes GitHub Actions
PhD Physics MBA Staff Eng @ Uber

How a 3–6 month engagement works.

No retainers, no ambiguity. A fixed arc with defined deliverables at every phase — and a clear handoff when we're done.

What Your Team Keeps

  • Production AI infrastructure, fully deployed
  • Architecture decision records with rationale
  • Runbooks for every system we build
  • CI/CD pipelines with automated testing
  • Monitoring and alerting configuration
  • Trained team capable of owning everything

Ready to replace your next five-year hire with a three-month engagement?

If you have a concrete problem — a system that needs building, a team that needs unblocking, AI that needs to actually work in production — let's talk.

For company engagements → westoverlabs.eu