Consulting

Senior engineering leadership for complex systems (AI included, not required)

I help teams ship secure, reliable software—cloud platforms, distributed systems, and AI capabilities (LLM/RAG/evals) when they deliver real business value.

My focus is production outcomes: clear architecture decisions, speed of delivery without regressions, cost/performance, security/compliance, and an approach that fits how your organization actually operates.

How I work / Engagements

Fractional CTO / Senior Engineering Leadership

Hands-on leadership for strategy, technical direction, architecture decisions, and delivery standards—when you need senior support shaping systems and teams.

Architecture direction + decision records (tradeoffs made explicit)
Roadmap shaping + delivery operating model (quality gates, reviews, rollout)
Mentoring for senior engineers and team enablement
Risk review: security, reliability, and cost (including AI-specific risks where relevant)

Architecture Review (Cloud / Distributed Systems / AI)

A structured assessment of your current system (or planned design) with concrete recommendations your team can execute.

Findings + prioritized recommendations (now/next/later)
Reference architecture + key tradeoffs (build vs buy, data flows, boundaries)
Plan for reliability, observability (logs/metrics/traces), and cost control

Delivery Sprint (production-grade thin slice)

Ship a focused, valuable increment end-to-end—so you get working software plus a pattern your team can repeat.

One production-ready feature (e.g., event-driven workflow, platform capability, RAG endpoint, or evaluation harness)
CI/test coverage + a deployment path your team owns
Monitoring/alerts + operational runbook basics
Security-first defaults (RBAC, data handling, auditability where needed)

Developer Productivity & Safe AI Tooling

Make your engineering org faster without sacrificing quality—workflows, standards, and (optionally) AI coding tools like Cursor/Claude Code.

PR/review standards, coding guidelines, and templates tailored to your repo
CI quality gates (tests, lint, security checks) tuned to your delivery flow
AI coding tool enablement: onboarding, guardrails, and regression prevention

How I approach delivery

Start with goals and constraints

We map the real workflow, stakeholders, and constraints (people, systems, risk, compliance) before choosing tech.

Design for operability from day one

Security, observability, cost controls, and failure modes are part of the architecture—not a later phase.

Ship in iterations with measurable outcomes

Small releases, clear success metrics, and monitoring (and evals where AI is involved) so quality improves over time instead of drifting.

Good fit if you’re building…

AWS-based platforms (serverless, containers) that need clear architecture and cost control
Event-driven systems and real-time pipelines (Kafka/Flink-style problems)
Multi-tenant SaaS with strong security, auditability, and compliance requirements
Reliability/performance work: SLOs, observability, incident-driven improvements
AI features that must be safe and measurable (LLM platform, RAG quality, evals)

Want to discuss a project?

The fastest way is booking a short call. If you prefer, you can also send a message with context: your goal, current state, constraints (security/compliance), and timeline expectations.

Book a call