Machine learning engineers
The discipline that existed before the current wave and still does the work underneath it. Forecasting, ranking, classification, fraud and recommendation systems rarely need a foundation model and always need someone who has kept a model honest in production.
- Typical seniority
- Mid-level through principal
- Core stack
- Python, PyTorch, scikit-learn, Spark, feature stores
- Platforms
- SageMaker, Vertex, Azure ML, self-managed
- Engagement shapes
- Single engineer or embedded pod
- Typical duration
- Three to twelve months
- Deliverable
- Models with monitoring and a retraining path
Machine learning engineers from CloudOps Network build and operate production ML systems: feature pipelines, training infrastructure, model serving, evaluation, drift detection and monitoring. They are matched on models that ran in production and were maintained afterwards, rather than on notebook work.
What ML engineers do on our engagements
Feature pipelines
Reliable, versioned features with the same computation in training and serving, which is where most production errors originate.
Training infrastructure
Reproducible runs, experiment tracking, hyperparameter search and a cost ceiling on the whole thing.
Model serving
Latency targets, batching, autoscaling, versioning and a rollback that does not require a deployment.
Evaluation
Offline and online, with the business metric attached, not just the loss curve.
Drift and monitoring
Input drift, prediction drift and performance decay, alerted before a stakeholder notices.
Retraining
A scheduled, tested path back to production, so the model does not quietly age into uselessness.
When you want ML rather than a foundation model
Foundation models absorbed a lot of attention that classical ML should have kept. For structured prediction on your own data, a trained model is usually cheaper, faster, more accurate and far easier to explain to a regulator.
A good ML engineer will make that recommendation even when it is the less fashionable one.
Structured, tabular data
Gradient boosting still wins most of these, at a fraction of the inference cost.
Strict latency budgets
Single-digit millisecond serving is routine for a trained model and difficult otherwise.
Explainability requirements
Regulated decisions need attributable reasoning, which trained models give you more readily.
High request volume
At scale, the cost gap between a hosted model and your own is decisive.
A sample of the bench
Availability changes weekly. This is the shape of who is in the network, not a live list.
Illustrative profiles drawn from the current roster. Names and clients are withheld until a mutual NDA is in place; the shortlist you receive names real people with real references.
Questions about hiring ML engineers
Do your ML engineers do data science as well?
Some do both. Most are stronger at productionising and maintaining models than at exploratory analysis, and the shortlist note will say which one a given engineer is.
Can they work with our existing ML platform?
Yes, across the major managed platforms and self-managed stacks. Tell us what you run and we filter for production experience with it.
What if our models have no monitoring today?
That is the common starting point. Instrumenting existing models is usually the first workstream, because it is the only way to tell whether anything after it helps.
Do you handle MLOps as well as modelling?
Yes. For most teams the pipeline and serving work is the larger share of the engagement, and a model without a retraining path is a liability with a deadline.
Tell us what you are building
Describe the work in a few lines. We come back with a shortlist of engineers who have done it before, their availability, and a rate range. No account, no platform fee, no obligation to hire.
Most briefs get a shortlist within three business days.