Embedded Reliability Engineers
One engagement path into GatekeeperOps, and not the main one. Where production AI work needs sustained ownership inside your delivery team rather than another external project, senior production engineers work inside your repositories, your CI/CD and your operating model to own a defined production workstream.
One engagement path, not the business model
GatekeeperOps is an AI production engineering practice. Most of the work here is bought as a scoped engagement against a production problem. Some production work is better held inside the client team instead, and that is what this page describes.
Production AI work sometimes needs sustained ownership inside the team rather than another external project. An embedded engineer does the same work the engagements do, architecture, integration, release controls, observability and recovery, owned directly alongside your engineers rather than advised on from outside.
What makes it work is engineering depth rather than reach. The people who do this have carried AI systems into production and operated them there, and the standard is the one applied to our own delivery.
For teams bringing the capability in-house
For teams with the engineering leadership to direct this work, and constrained capacity to execute it.
Production engineering for AI sits across architecture, integration, release controls, observability and recovery. Carrying a production workstream across all of that needs someone inside the team for long enough to hold it, working in the system rather than reporting on it from beside it.
An embedded engineer joins your delivery team under your engineering lead, works in your repositories, your pipelines and your operating environment, and leaves the work behind in them. This is direct technical ownership, not advisory capacity beside your team.
| Who asks for this | What they are solving |
|---|---|
| CTOs and VPs of Engineering | Production ownership added to the team without adding permanent headcount |
| Heads of Platform or Engineering | Depth on reliability and release control the team does not hold yet |
| Engineering Managers | A specific AI system that has to reach production on a known date |
| Founders at Series A to B | Senior production engineering during scaling, held inside the team |
Engineering depth
Depth is described by what the engineer is expected to own rather than by a public grade. The variables are the autonomy the workstream needs, how much architecture sits inside it, how much production responsibility comes with it, and how much operating context has to be carried before the work can move.
How the engagement runs
- 1.Define the workstream. Identify the production responsibility, systems involved, required engineering depth and ownership boundaries.
- 2.Match the engineering depth. Select the production engineering profile appropriate to the architecture, systems, autonomy and workstream.
- 3.Integrate into the team. The engineer works inside your repositories, CI/CD, delivery workflow, engineering rituals and operating environment.
- 4.Deliver and operate. The engineer owns the agreed production work alongside your team, with GKO practice oversight available where appropriate.
- 5.Transfer or continue. Continue the embedded engagement, change scope, reduce capacity or transfer the production context back to your team.
Engagement Models
| Model | When It Fits |
|---|---|
| Embedded | The engineer joins your team and reports to your engineering lead. You direct the work. |
| GKO-managed | We hold direction and delivery quality. You receive the output, not the management overhead. |
Investment
Embedded Reliability Engineers is scoped around the production workstream, required engineering depth, expected ownership, working model and engagement duration. Terms are agreed privately, and nothing here commits you to an engagement on any other page.
Book the AI Production AuditHow engineers are assessed
The assessment every engineer comes through before they work on a client system.
Stage 1: Background review
Engineering background, project history, production AI exposure, and the areas of depth the engineer claims.
Stage 2: Structured screen
Structured assessment of technical depth, written reasoning and communication clarity.
Stage 3: Practical exercise
A practical production-engineering exercise completed within an agreed window, which may cover architecture, evaluation, integrations, failure handling, observability or workflow design depending on the role. Reviewed against explicit criteria for technical depth, production reasoning, implementation quality and communication.
Stage 4: Technical discussion
A technical discussion focused on the decisions, trade-offs and production reasoning behind the work, extending it under questioning and aligning on expectations.
Stage 5: Final round
A conversation on communication style, client mindset, availability, and the kind of workstream the engineer is ready to own.
The bar is deliberately high and very few applicants clear it. This path exists for engineering depth, not for volume.
For Engineers Applying
If you have carried an AI system into production and kept it there, and you want engagements that match that, applications are open.
The work is production engineering on systems whose outputs cannot be predicted in advance: new model behaviour, new retrieval architectures, new agentic patterns, new failure modes. It sits closer to reliability engineering than to feature work, and it does not repeat.
Who this is for
| Type | Profile |
|---|---|
| Senior engineers moving into production AI | Strong automation and delivery foundation, building evaluation depth |
| Platform and reliability engineers | Observability, release control and incident work applied to AI systems |
| Evaluation and retrieval practitioners | Already working on LLM evaluation, retrieval quality, agentic behaviour |
| Red-teamers and security engineers | Adversarial testing and failure injection applied to AI |
What the work offers
| Benefit | Detail |
|---|---|
| Selective engagements | Matched to the production work that fits your depth, expertise and availability |
| Compensation | Reflects the scope, complexity and ownership of the workstream |
| Methodology access | The delivery model and the internal playbooks used on engagements |
| Brand co-building | Engineers can publish under own name with GKO affiliation |
| Project variety | Different AI features, model stacks, problem domains |
| Async culture | IST-respecting hours, no overnight calls |
| Growth path | Larger workstreams and more production ownership, based on demonstrated capability |
How to Apply
- 1.Submit application form.
- 2.Applications reviewed in batches. Strong profiles invited to take-home assessment when next review cycle opens. Where possible, brief feedback provided.
- 3.Practical take-home exercise completed within an agreed window.
- 4.Technical discussion focused on the decisions, trade-offs and production reasoning behind the work.
- 5.Final round.
- 6.Onboarding into the practice.
Honest Expectations
This path is selective by design. Applicants who are not ready for the work can apply again after building further production experience. The bar does not lower over time.
Bring senior production engineering inside your team, on your systems, under your lead.
Book the AI Production AuditWork on AI systems that have to hold up in production.
Apply to join