I’ve spent the last decade building large-scale infrastructure at Netflix, Twitter, and Comcast - systems handling hundreds of millions of users and billions of requests per day.
The pattern recognition from operating at that scale is what helps small companies avoid mistakes before they become crises.
Outcome: Written assessment identifying risks and bottlenecks, target architecture aligned with your goals (delivery speed, operational safety, scaling), and sequenced roadmap with milestones.
Timeline: 1–2 weeks for assessment and plan. Implementation support available.
What I do: Code/infrastructure review, stakeholder interviews, written recommendations, optional hands-on implementation leadership.
Outcome: System architecture for your ML training pipelines, model serving, feature stores, or experimentation platforms—designed for production scale and operational reliability.
Timeline: 2–3 weeks for design + documentation. Implementation support available.
What I do: Understand your ML workflows and business requirements; design data pipelines, training infrastructure, and serving architecture; document system design with data flow diagrams and technology recommendations; provide capacity planning and scaling strategy; optionally lead implementation with your team.
Outcome: Written evaluation of your failure modes and recovery capabilities, with a prioritized roadmap for reducing outages and improving incident response.
Timeline: 1–2 weeks for assessment + roadmap.
What I do: Analyze architecture for single points of failure; review retry logic, timeouts, and circuit breakers; assess monitoring, alerting, and runbooks; test failover scenarios; document findings with sequenced improvement plan; implementation support available.
Outcome: Comprehensive technical assessment of acquisition targets or investment opportunities—evaluating scalability, technical debt, operational maturity, and integration risks to inform your decision.
Timeline: 2–4 weeks depending on system complexity.
What I do: Review codebase, architecture, and infrastructure; interview engineering leadership; assess data pipelines, deployment practices, and operational capabilities; evaluate technical debt and scaling bottlenecks; analyze team structure and engineering practices; deliver written report with risk assessment, cost implications, and integration recommendations.
First call (30–45 minutes): We’ll discuss your problem, review constraints, and explore potential approaches. I’ll ask technical questions; you’ll get a sense of how I work. If it’s a fit, I’ll send a brief proposal.
First engagement: Usually a 1–2 week assessment delivering written findings and a prioritized roadmap. This provides immediate value and determines whether deeper implementation work makes sense.
Note: For straightforward problems, I sometimes send probing questions before we talk—this helps me provide a rough approach and ensures our time together is well-spent.