Production Engineering
Most 'MVP' engagements stop at a demo. I build the fully-complete, production-grade version — architected properly, shipped to real infrastructure, and handed over in a state your team can run without me. One person accountable from the product decision through to the production deploy, not a relay race across four different contractors.
- Discovery and prioritisation tied to outcomes, not feature requests
- Platform and domain design — monolith, modular monolith, or service-oriented, chosen for the stage
- Architecture decision records on every load-bearing call, so the reasoning survives me leaving
- Full-stack delivery: TypeScript/Node, Python, Java; React and Next.js; PostgreSQL and Redis
- Data platforms on Microsoft Fabric and Azure — PySpark pipelines, medallion lakehouse, a semantic layer product and AI features both query
- AI features built properly: model selection, fine-tuning, RAG, and agent orchestration, with eval harnesses so they don't regress silently
- Terraform-first infrastructure-as-code across AWS, Azure, or GCP — cloud-agnostic by default
- CI/CD with preview environments, observability, and SLOs wired in from the first deploy
- Delivery rhythm and DORA metrics set up to survive without me in the room
Blank team vs an embedded production engineer
The ranges below are what each stage typically takes from a standing start. Working with me compresses them because architecture, build, and infrastructure run as one accountable thread instead of handoffs between separate hires.
No handoff between product, build, and infrastructure roles — nothing waits in a queue between phases the way it does across separate hires or contractors.
No ramp period. Full-pace contribution from the first sprint, on a stack I'm also accountable for running afterwards.
IaC, CI/CD, and observability get built alongside the feature work, so 'production-ready' is true on day one rather than a separate project bolted on at the end.
Ranges assume a single product surface on a green-field or lightly-established codebase. A large legacy migration or several parallel surfaces push every figure to the right.
A build that's stalled, an architecture call you can't get wrong, or an AI feature that needs to leave the prototype stage — tell me where it stands.
- Architecture review + ADRs for the load-bearing decisions
- Production system live on your infrastructure, under CI/CD
- Data or AI features shipped with eval harnesses, not just a demo
- Delivery playbook and metrics dashboard your team owns after I leave
Ongoing retainer or project-shaped build depending on scope. Typically opens with a 2-week architecture and discovery pass, then continuous delivery through to production.
Questions I get asked about this.
Do you write the code yourself, or manage a team that does?
Both, depending on scope. I ship production code every week myself — that keeps the architecture honest, because I'm also the one accountable for living with it. Where there's an existing team, I work alongside them rather than replacing them.
What if I already have engineers?
Then I plug into the gaps — usually architecture ownership, the AI or data layer, or infrastructure that's been deferred too long. I'm not there to duplicate a working team, only to own the parts that need a senior, accountable hand.
Is this different from hiring a dev agency?
An agency bills hours and hands you a deliverable. I own outcomes on infrastructure I'm also responsible for handing back to your team in a state they can run — architecture decisions documented, CI/CD in place, no bus-factor of one held outside the company.
Do you handle the AI/data parts specifically, or just general engineering?
Both, on the same platform. Data pipelines, model selection, RAG, and agent work go through the same production discipline as the rest of the build — eval harnesses, observability, and rollback paths — not a separate proof-of-concept that never gets hardened.
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Ready to scale your engineering?
Book a 30-minute discovery call. If we're not a fit, I'll tell you on the call — and point you toward someone who is.