Hire MLOps engineers for the model that already shipped
Most AI budget goes into building a model and almost none into watching it after launch. Our engineers build that layer: monitoring, drift detection and retraining paths. You interview them before anyone starts.
What you are getting
4 things that decide this
- 01Senior engineers who instrument prediction quality in production, beyond uptime and response time.
- 02You interview every engineer before they join. Nobody is assigned to your team from a profile you never saw.
- 03They automate retraining triggers instead of leaving your team to remember a calendar date, and they treat the data pipeline feeding the model as the thing most likely to break.
- 04If the fit is wrong, say so and we replace the engineer. Every pipeline and every line of config is yours from the first commit.
What an MLOps engineer actually does here
This is not the person who trains the model. It is the person who finds out when it stops being right, and builds the path to fix it without a fire drill.
On TankAware they built the IoT ingestion and alert pipeline for Sutherland Excavating. It flags tank and site anomalies before they turn into failures, across hundreds of sites. On Greenlight they built the scoring pipeline behind an ESG research platform. It weighs AI judgment against source data across more than 50 topics per company, so a score stays defensible as sources shift under it.
Different domains, one repeated job: prove the pipeline behind the prediction is still telling the truth, and catch the day it stops.
What they take off your roadmap
Production monitoring that grades answers
A model can return a confident, well-formed response while quietly getting more of them wrong. Uptime dashboards do not see this. Ours are built to.
Drift detection on the inputs, before accuracy visibly falls
The data reaching a model usually shifts before the error rate does. They watch the inputs as well as the outputs, and alert on the earlier signal.
Retraining triggers instead of a calendar reminder
A quarterly retrain either fires too late or wastes a cycle on a model that had not moved. They wire retraining to measured drift, so it fires when the data actually calls for it.
A pipeline that survives the engagement
The ingestion code, the monitors and the graded evaluation set stay in your repo, documented, with someone on your team trained to run them.
- InstrumentLog inputs, outputs and the trace behind each one
- Watch inputsDrift shows here before accuracy drops
- Grade outputScored against cases your experts wrote
- Trigger retrainOn measured drift, not a calendar date
- Re-gradeNew failures become new test cases
Most AI spend stops at launch. This is the layer that decides whether the model is still worth trusting six months in.
Pipelines we still watch after launch
How hiring works
- 01
Tell us what is stuck
A free call about the model, its data pipeline, and what happens today when it gets something wrong. If nothing is live yet, you will hear that this is a build, not an MLOps engagement.
- 02
Meet the engineers
We shortlist people who have instrumented a live pipeline for drift and retraining, and you interview them. Say no and we go back to the shortlist.
- 03
They embed
Your repo, your standups, your alerting stack. One engineer owns monitoring quality and is named as the person accountable for it.
- 04
They hand over
The monitors, the retraining runbook and the graded evaluation set, plus someone on your team trained to run them. Where a client would rather we kept watching drift, we do that under an agreed service level. The first 2 months of support and maintenance are free, with every build.
Stack
Models
Pipelines
Run and watch
Tell us what your model is doing quietly
Bring the pipeline as it runs today, the prediction it makes, the data that feeds it, and how you would currently find out it got worse. The scoping call is free.
01How is this different from hiring a data scientist?
A data scientist is often the person who built the model. An MLOps engineer is the person who keeps it accountable after launch: monitoring, drift detection, retraining paths and the release process a change goes through. Teams frequently have the first and are missing the second entirely.
02What if the engineer is not the right match for our pipeline?
You interviewed them, so this is rare. Say the word and we swap them out. It stays our problem to solve rather than a hiring cycle you have to run again.
03How much overlap do we get with our working day?
A daily overlap with your hours, fixed before anyone starts. Drift alerts and retraining decisions need a conversation while both sides are awake, not a ticket answered a day later. We agree the window during scoping and it does not move without you.
04Who owns the monitoring code and the evaluation set once we stop working together?
All of it is yours from the first commit, including the graded test cases that tell you months later whether a model has drifted. Nothing sits in a tool only we can access.
05Our model already works. Why would we need this now?
Because a model can degrade with no code change at all. The data it sees keeps moving while the model stays fixed. A confident wrong answer looks identical to a correct one until someone is watching for the difference. Scoping calls are free. Where we need to get into an existing pipeline before answering honestly, a paid two-week diagnostic produces a fixed price rather than a guess.
