Senior engineers who join your team and stay
You interview every one of them before they start. They work in your repository, under your review process, and they stay on after launch.
What you are buying
4 things that decide this
- 01You interview every engineer before they join your team, and you can decline any of them.
- 02They work in your repository, your standups and your review process. Your engineers approve their pull requests.
- 03Senior only. We do not put a junior on your project to fill a seat.
- 04They stay after launch under an agreed service level, or they train your team to take it over.
Most staffing goes wrong at the same two points
The first failure is who arrives. A profile gets approved, a different person joins the standup, and by the time you notice, two sprints are gone. The fix is boring and it works: you meet the engineer first.
The second failure is what happens at the end. The contract closes, the engineer moves on, and the thing they built has nobody who understands it. AI work makes this sharper. Every model behind a production system gets retired eventually, and a team that has already left cannot tell you whether the replacement still passes.
- 01Your review process stays yours. Nothing merges because an outside lead approved it.
- 02Code sits in your repository from the first commit, not in ours until final payment.
- 03One engineer, or a group. You set the priorities either way.
- 04You can end it. No penalty clause for deciding the fit is wrong.
What we staff
Every role here is filled by a senior engineer who has shipped the thing in production, not studied it.
AI and agent engineers
People who have built systems that call tools and change state, and who know what to do when a step fails halfway through.
Hire AI developers →
Claude and LLM engineers
Engineers who have taken the Claude API and MCP into production, including the evaluation suite underneath.
Hire Claude developers →
Retrieval engineers
Chunking that survives a scanned PDF, permissioned retrieval, and evals that catch quality drift before a customer reports it.
Hire RAG engineers →
Product engineers
React, Python and mobile. An AI feature still needs an interface and a backend that hold up under real traffic.
- ScopeFree call. What you need, and who fits.
- InterviewYou meet them. You can decline.
- EmbedYour repo, your standups, your reviews.
- ShipPull requests your team approves.
- StayAgreed service level, or trained handover.
The interview step is the one a marketplace cannot offer you. A profile and a rating are not the same as meeting the person who will write the code.
The question a rate card cannot answer
Ask anyone you are about to hire what happens when the model your system runs on is deprecated. Prompts behave differently across model versions. A team that has not planned for it will find out under pressure, with your users watching.
The answer is an evaluation suite. Cases drawn from real failures, run against the new model, so the swap becomes a decision instead of a gamble. Our engineers build that alongside the feature, because retrofitting it after an incident is how teams end up rewriting.
- Evals written from the failures your system actually had.
- A runbook your team can follow without calling us.
- Monitoring on cost, latency and answer quality, from day one rather than after the first incident.
The stack
AI
Backend
Frontend and mobile
Run
“They will treat your vision like their own and build it that way.”
Ron Klabunde · Founder, SmartREI ↗
A marketplace against an embedded engineer
| Criterion | Marketplace or job board | Embedded with us |
|---|---|---|
| Who you meet | A profile, a rating and a filter percentage. | The engineer, in an interview you run. Decline anyone. |
| Where the code lives | Sometimes theirs until the invoice clears. | Your repository from the first commit. |
| Who reviews the work | Nobody, or a delivery manager you never meet. | Your engineers, in your pull request process. |
| Evidence they can do it | Hours billed on the platform. | Named systems in production, with the mechanism described. |
| When the model is deprecated | The engagement ended. Your problem. | An eval suite that tells you whether the swap is safe. |
| After launch | The contract closes. | An agreed service level, or your team trained to run it. |
Questions, answered
01What is IT staff augmentation?
IT staff augmentation adds outside engineers to a team you still run, instead of handing a project to a vendor to deliver. You keep the roadmap, the review process and the definition of done. At Hashlogics every augmented engineer is senior, works in your repository, and is interviewed by you before they start.
02Do we interview the engineers ourselves?
Yes, and you can decline any of them. You meet the specific person who would do the work, not a representative profile. Any vendor who resists this is telling you something.
03How is this different from outsourcing a project?
Staff augmentation gives you people; outsourcing gives you an outcome. With augmentation you set priorities sprint by sprint and your team reviews the code. Outsourcing suits a bounded piece of work you would rather not manage at all.
04What if an engineer is not the right fit?
Tell us and we replace them. The interview exists to make that unlikely, but a bad match is our problem to fix rather than something you are locked into for the rest of a contract.
05Who owns the code they write?
You do, completely, from the first commit. Source code, prompts, models and data pipelines are yours. Nothing is licensed back to us and nothing depends on us to keep running.
06Which time zones do they work?
You get at least four hours of overlap with your working day, agreed before anyone starts. Most clients take a longer overlap so standups and code review happen live rather than overnight.
07What does it cost?
Scoping is free, and you get a written scope before you commit to anything. Where we have to go into an existing codebase first, a paid two-week diagnostic sets a fixed price for the work that follows. We are not the cheapest bid and do not try to be.
08Can you cover both AI and normal product work?
Yes, and most engagements need both. An agent that reads your documents still needs a working interface, a backend that holds up, and someone to keep both running after the launch.
Related
- Agency vs in-house AI team →When hiring your own people is the better call.
- Staff augmentation vs outsourcing →People you direct, or an outcome you buy.
- Hire Claude developers →Engineers who have shipped Claude to production.
- From prototype to production →What taking a prototype into production actually involves.
