Services

My engagements can be hands-on, or purely guidance and consulting. Examples of my earlier engagements: bringing large projects over the finish line, setting up telemetry, setting up alerts and on-call rotation, and working hand-in-hand with your engineers during incident resolution. You can contact me for an analysis of your system and get written findings that you can act on, whether or not we keep working together.

Fixed-scope offers

• Cyber Resilience Act readiness: reporting pipeline, SBOM factory, technical file.
• EU AI Act compliance: inventory, Article 50 in your product, high-risk readiness.
• Ransomware restore proof: clean-room drills, an immutable vault, insurer evidence.
• Cloud and AI bill reduction: invoice autopsy, executed fixes, success fee.
• Agent hardening: red team, evals in CI, identity and kill switches.

AI-assisted engineering

LLM coding tools can be great with proper use, but unreliable in real large codebases. How good a model is depends on the tooling, the context, and the verification around it. I work on making AI and LLM productive in an engineering organization: context engineering, setups with large mono-repos or multiple focused repos, preparing and maintaining repository guidance files, AI review systems, agent workflows with verification built in, and measurement of actual throughput improvements. My preferred way of working is enabling developers and AI agents to fully and quickly develop and test locally (1-10s max per change-deploy-test cycle), containerize everything, do additional extensive tests on the CI with the containers in a staging environment, and if all tests pass - automatically deploy to production with a one-button rollback procedure. At Caffeine.ai I built the custom LLM PR-review system that became the team’s main review system, and an AI-native ticket-to-PR loop (integration with Linear). The loop covers everything between plan, implement, self-review, fix, update ticket, and respond to reviewer comments. My setups can achieve sustained 5–15 meaningful PRs per engineer per day while burning down tech debt.

What you can get: anything from hands-on building to plain analysis and guidance. Context engineering fitted to your codebase; review automation tailored to your standards; agent workflows for the work worth automating; and numbers that show whether any of it is paying off.

Production reliability

Production systems fail in ways that are obvious in hindsight and missing from the dashboards, until someone fixes the dashboards. I work on observability, incident reduction, rollout safety, migrations without downtime, and on-call and operations maturity. At DFINITY I led the reliability team for the Internet Computer, and my team managed the 1,400+ node HA network: critical incidents (with parts of the IC network getting stalled - IC provides CP from the CAP theorem) went from double digits per year to 1–2 while usage grew by orders of magnitude and we shipped hundreds of network upgrades per year. My team built observability on bare-metal Kubernetes across three Availability Zones, and before that two more colleagues and I rewrote the CI system that delivers reproducible builds and runs all tests, cutting merge times from 1–2 days to 30–60 minutes.

What you can get: working telemetry with alerts that people trust, a measured drop in incident count and severity, rollouts you can reverse, and migrations that do not need a maintenance window. If desired, my team can fully take over the production deployment and maintenance from you, so you can redirect your core resources from building the observability and production deployments, and focus on what really matters to your business.

Storage and distributed systems

Storage is where systems succeed or fail: latency budgets, durability, failure modes, and the trade-offs that only show up under real load. I do architecture reviews and design work for storage engines, data platforms, and the distributed systems underneath them, from flash firmware behavior up to cluster-level design. At IBM Research I was one of the core FTL firmware developers for the IBM FlashCore Module. Later I architected and partly built an analytics platform ingesting real-time telemetry from 11,000+ enterprise storage systems. That grounding comes from a research career: 16+ peer-reviewed papers and 60+ patents.

What you can get: an architecture review of your entire stack, written findings with concrete improvement recommendations, and a design your engineers can implement.

Working together

Engagements range from a short assessment with written findings to ongoing advisory. If any of this sounds like your problem, find me on LinkedIn.