Profile & Capabilities
Production AI infra consulting: GPU clusters, Kubernetes + GitOps workflows, cost baselines, and operational guardrails so deploys stop feeling risky.
Recent outcomes
- Shippable artifacts (dashboards, runbooks, baselines) as default deliverables: readiness audit.
- Proof you can click through (not marketing diagrams): live demos.
- Notes on constraints, verification, and what changed: case studies.
Capabilities
What I do
The work is usually a mix of platform engineering and operational design. I focus on the places where teams lose time: unclear bottlenecks, risky deploys, and dashboards that don't match the on-call questions.
- GPU platform build: scheduling, routing, scaling, day-2 ops
- Cost baseline: throughput vs spend, cache/batch/routing levers
- GitOps reliability: safer rollouts, environments, rollbacks
- Observability: signals, alerts, and runbooks tied to ownership
What you get
I try to ship artifacts you can reuse after I'm gone: baselines, docs, dashboards, and an execution plan that fits your constraints.
- Baseline report: workload profile + bottlenecks + cost curve
- Deployment model: topology, capacity, and scaling strategy
- Dashboards + alert thresholds (first pass)
- Runbooks: alerts -> actions
- 90-day plan: owners, milestones, and risk register
Typical engagement
Agree on the workload, instrument the right signals, and produce a cost/latency baseline you can reproduce.
Put guardrails in place: routing, budgets, autoscaling, and rollout/rollback with clear abort conditions.
Dashboards + alerts that map to on-call questions. Runbooks tied to ownership, plus a practical roadmap.
Tight loops: measure, change one thing, validate. Reduce surprises and keep spend predictable.
Background
Prior roles that shaped the ops-first approach: integrations, reliability, and shipping under SLA constraints.
Let's talk
Tell me what you're building, your constraints, and what you've tried. I'll respond with a concrete next step (even if that means “don’t hire me for this”).