The forward-deployed engineer who owns the machine learning layer: data pipelines, training, evaluation, deployment and monitoring, built inside your team and handed over documented.
Why this role exists
A notebook result is easy. Keeping a model honest against fresh data, inside a latency and cost budget, with a rollback you trust, is the hard part, and it needs an engineer who lives in that loop.
Most teams do not need an ML platform team. They need one senior engineer who owns features, training and serving end to end, works inside their codebase, and leaves the pipeline 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 ML engineer owns the part of the product where data becomes predictions: feature pipelines, training, evaluation, serving and monitoring. They write the code, run the experiments, and stay answerable for how the model behaves in production.
What to verify: Ask which parts of the pipeline the engineer will personally own, and who reviews changes before they reach users.
Models are only as good as their inputs. They build pipelines with versioned datasets, quality checks and transforms that can be re-run months later, so every model can be traced back to the exact data that produced it.
What to verify: Ask how a training run can be reproduced, where datasets are versioned, and what happens when a source changes shape.
A model in a notebook helps nobody. They put models behind versioned endpoints with latency and cost budgets, canary releases and a rollback that works, so an improvement can be reverted in minutes when it misbehaves.
What to verify: Ask how a model is deployed, how versions are tracked, and how long a rollback takes when a release goes wrong.
They build an evaluation set from your real cases and run it on every change, so a new model is judged on evidence against a baseline. Offline numbers and live behaviour are reported together, not cherry-picked.
What to verify: Ask to see the evaluation set, the baseline it is compared against, and what numbers gate a model from test to production.
Models decay as the world changes. They track input distributions, prediction quality and serving health, alert when drift appears, and make retraining a deliberate decision with evidence rather than a calendar reflex.
What to verify: Ask what is monitored after launch, who gets alerted, and what triggers a retraining decision.
Every pipeline, dataset, model card and decision lives in your repository with written rationale. When the engagement ends, your team runs, retrains and improves the system without us in the loop.
What to verify: Ask where pipelines and model documentation live, and who on your side can retrain a model after handover.
How they work
The loop is simple and strict: collect the data, train against a baseline, evaluate on real cases, deploy behind a switch, and monitor for drift before users find it.
AI in the engagement
AI is not a separate offering bolted on. It is the material this engineer works in, and the judgment they bring is part of what your team absorbs.
A simple model sets the bar. The engineer proves what a straightforward approach achieves first, so any added complexity has to beat it measurably, not just look impressive.
AI assistants speed up scaffolding, tests and exploratory analysis. What gets trusted, shipped and monitored is decided by the engineer, with the evidence written down.
Every wrong prediction in production is captured, labelled and folded into the evaluation set, so the same failure class is caught before the next release.
Predictions that move money, health or customer commitments surface a confidence and an explanation, and the consequential action stays with a person.
Working principles
Written scope, reviewed changes and visible trade-offs decide whether an embedded ML engineer actually works. We make those commitments explicit rather than implied.
The model's task, data sources, metrics and serving boundaries are written down before the first commit, and changes to them are agreed, not assumed.
Feature changes, training changes and serving changes go through the same review and evaluation gate as code. Nothing reaches users because it 'scored better once'.
When accuracy, latency and cost conflict, the engineer shows the numbers 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 training purposes are agreed in writing before the first commit.
The engineer gets scoped access to the data and systems the work needs, nothing more, and access is reviewed and revoked at the end of the engagement.
Datasets, features and trained models live in your storage and your registry. Nothing is retained on our side once the engagement ends.
Personal identifiers are minimised, pseudonymised or excluded from training sets as agreed, and every use of personal data is documented.
Training runs, releases and rollbacks are logged so any prediction a model version made can be traced after the fact.
Working arrangements
The engineer works remotely inside your rituals, in your cloud and your repositories, with training and serving running on infrastructure you control.
The engineer joins your stand-ups, reviews and planning from remote, and works in your repositories and issue trackers as a team member would.
Training and serving run in your accounts, with your keys and your logging. We do not route your data through infrastructure you have not approved.
What the pipelines may read, what may leave your environment and what must never leave are agreed in writing before development starts.
For workshops with your domain experts, data access reviews or a critical go-live, 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 decisions: what the model must predict, which data exists, where a wrong prediction hurts, and what the current baseline is. The engineer sits with your team and reads the actual workflows.
One goal in writing: for example, a working model for one decision, trained on your data, with a defined evaluation set and a serving path behind a switch. Success criteria are agreed before building starts.
The engineer builds inside your codebase, runs the evaluation set on every change, and shows you the real results, including the failures, at the end of each week.
At the end of the sprint you see the evaluation numbers, the serving cost and the failure cases, and decide together what comes next: extend, hand over or stop.
The ones the decision needs: forecasting, classification, ranking, recommendation, anomaly detection and matching problems, plus fine-tuning when the evidence justifies it. The engineer picks the simplest approach that meets the bar and proves why anything heavier is warranted.
Yes. The role exists to work inside your team, so they build in your languages, your framework and your cloud, and use the tooling your compliance rules allow.
Not necessarily. Week one includes an honest look at what data exists and what labels it needs. Where labels are missing, the sprint goal is chosen so that labelling or evaluation can start with what you have.
Input distributions and prediction quality are monitored from day one. When drift appears, you see the evidence, and retraining is a deliberate decision with the numbers in front of you, not an automatic job nobody reviews.
No, but someone on your side owns the outcome. The engineer works with your domain experts to define what a good prediction looks like, and hands the system to your team with documentation, pipelines 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 decision, the data and the constraints. We will help define a first sprint goal and the evidence needed to judge it.