The forward-deployed engineer who owns the server side: APIs, data, jobs and reliability, built inside your team and handed over documented.
Why this role exists
A happy-path demo is easy. An API that stays correct under load, a migration that runs safely on live data, and a job queue that never loses work is the hard part, and it needs an engineer who lives in that layer.
Most teams do not need a platform team. They need one senior engineer who owns APIs, data and reliability end to end, works inside their codebase, and leaves the system running without us. That is exactly what this role is.
What this engineer owns
The engagement starts with a single senior engineer inside your team and grows only when the problem justifies it. These are the commitments behind the role; exact scope is agreed with your team.
Your backend engineer owns everything behind the interface: APIs, data models, background jobs, integrations and the infrastructure glue between them. They write the code, run the operations, and stay answerable for how it behaves under load.
What to verify: Ask which services, queues and data stores the engineer will personally own, and who is paged when they misbehave.
Endpoints with versioned contracts, permissions enforced on the server, and changes that stay backward compatible or are migrated deliberately. Consumers of the API, including AI agents, get documentation they can rely on.
What to verify: Ask how API changes are versioned, where contracts are documented, and how breaking changes are rolled out.
Schema design for the queries the product actually makes, migrations that run safely on live data, and indexes justified by measurements. The data layer is designed to be changed, not to be feared.
What to verify: Ask how migrations are applied to production, how rollbacks work, and what proves a query performs at scale.
Queues with retries, idempotent handlers, backpressure when a dependency slows down, and third-party integrations that degrade gracefully. Work that enters the system is tracked until it is done.
What to verify: Ask what happens when a job fails twice, when a provider is down, and how lost work is detected.
Metrics, structured logs and traces around the paths that matter, alerts that fire on symptoms users feel, and load tests before the traffic arrives. Uptime is a number the team watches, not a hope.
What to verify: Ask what is monitored, what alerts page a human, and what the last load test showed.
Every service, schema and decision lives in your repository with written rationale and runbooks. When the engagement ends, your team operates and extends the system without us in the loop.
What to verify: Ask where runbooks and architecture decisions live, and who on your side can add a feature after handover.
How they work
The loop is simple and strict: design the contract, build the service, verify it under real load, and operate it with the metrics that prove it works.
AI in the engagement
AI is not a separate offering bolted on. It is part of how this engineer works, and the judgment they bring is part of what your team absorbs.
AI tooling accelerates scaffolding, tests and boilerplate. What ships is code the engineer has read, understood and taken responsibility for.
The server side is where data gets destroyed and money gets moved. The engineer prefers designs that are obviously right over ones that are probably fast, and says so in writing.
AI-assisted changes go through the same review, tests and load checks as everything else. Nothing reaches production because it looked finished.
APIs and tools are built so AI agents can use them safely: explicit permissions, rate limits and audit trails, so automation through your systems stays inspectable.
Working principles
Written scope, reviewed changes and visible trade-offs decide whether an embedded engineer actually works. We make those commitments explicit rather than implied.
The services, data boundaries and reliability targets are written down before the first commit, and changes to them are agreed, not assumed.
Every change goes through review and CI like the rest of the codebase. The engineer is a team member, not an outside black box.
When consistency, latency and cost conflict, the engineer shows the options and lets you decide, rather than picking quietly.
You work with the same engineer for the engagement, not a rotating bench. Continuity is what makes judgment about your domain compound.
Security & trust
This role touches your data, your infrastructure and your customers. Access, data boundaries and deployment permissions are agreed in writing before the first commit.
The engineer gets scoped access to the repositories, environments and data the work needs, nothing more, and access is reviewed and revoked at the end of the engagement.
Real customer data is read where it lives under your controls. Copies for development are minimised, masked and deleted when the work is done.
Keys and credentials live in your secret manager, never in code or chat, and are rotated when the engagement ends.
Commits, deployments and access are logged, so any change to your systems can be traced to a person and a reason after the fact.
Working arrangements
The engineer works remotely inside your rituals, in your cloud and your repositories, with services and data running on infrastructure you control.
The engineer joins your stand-ups, reviews and incident calls from remote, and works in your repositories and issue trackers as a team member would.
Services, queues and databases run in your accounts, with your keys and your logging. We do not route your systems through infrastructure you have not approved.
What the engineer may access, what may leave your environment and what must never leave are agreed in writing before development starts.
For architecture workshops, a migration window or a critical launch, on-site time is arranged deliberately.
How it starts
A paid two-week sprint proves the working relationship before anything longer is discussed. If it does not show real progress, you walk away.
Week one starts with the real system: what the services do today, where the queues back up, which queries are slow, and what breaks first under load. The engineer sits with your team and reads the actual architecture.
One goal in writing: for example, one service rebuilt or shipped with tests, monitoring and a documented contract. Success criteria are agreed before building starts.
The engineer builds inside your codebase, opens small reviewed changes, and shows you the real results, including what did not work, at the end of each week.
At the end of the sprint you see what shipped, the load numbers and the failure cases handled, and decide together what comes next: extend, hand over or stop.
The ones your product runs on. The engineer works across the common server stacks and their clouds, and the point of the role is fitting into your codebase, not bringing a favourite framework.
Yes, and that is the default. They work inside your repositories, follow your conventions and improve the systems they find, rather than starting something parallel.
With measurements first. The engineer profiles the real path, identifies the bottleneck with numbers, fixes that, and re-measures, instead of rewriting on suspicion.
Yes. The engineer works within your platform's rules and reviews, and hands over anything touching infrastructure for your team's approval.
No, but someone on your side owns the outcome. The engineer works with your product owner or domain experts, and hands the system over with documentation, tests and runbooks.
The sprint is a fixed, paid engagement agreed before it starts, and its scope is written down. If it does not show real progress, you walk away with everything built so far in your repository.
Let's talk
Tell us about the system, the load and the constraints. We will help define a first sprint goal and the evidence needed to judge it.