Machine learning consulting — staffed with senior ML engineers
Most ML consulting engagements end with a slide deck and a recommendation. This is the other kind: engineers who scope the problem, write the code, train the model, and hand you something in production.
What this covers
Problem scoping
Before any model gets trained, the engineers spend time with your data and your actual business question. A lot of ML projects fail because the problem was framed wrong at the start — a classification problem treated as regression, a forecasting problem that needed a simpler statistical model. The engineers identify that early.
Model selection and training
They pick the approach that fits the data and the deadline, not the approach that’s most technically interesting. That might be gradient boosting over a neural network, or a fine-tuned open-source LLM over training from scratch. Training runs on your infrastructure or on cloud compute you control.
Evaluation harnesses
A model without a proper evaluation framework is a liability. The engineers build the holdout sets, define the right metrics (not just accuracy), and set up the test pipeline before claiming the model works.
MLOps and deployment
Getting the model into production is often more work than building it. The engineers handle containerization, API endpoints, monitoring, and retraining pipelines. If you have a data platform already, they work within it. If you don’t, they’ll tell you what you need before you need it.
Typical team
1–2 senior ML engineers, depending on whether you’re in the build phase or the iteration phase.
Optional: MLOps engineer — if deployment and infrastructure are a significant part of the scope (containerization, Kubeflow pipelines, SageMaker workflow, feature store setup), a dedicated MLOps engineer runs that track in parallel.
The MLOps engineer is rarely needed from day one. It becomes necessary when the model is ready for production and the infrastructure work is blocking progress.
Tech stack
| Area | Technologies |
|---|---|
| Core language | Python |
| Frameworks | PyTorch, TensorFlow, scikit-learn, XGBoost |
| Experiment tracking | MLflow, Weights & Biases |
| Orchestration | Kubeflow, Airflow |
| Cloud platforms | AWS SageMaker, Azure ML, Google Vertex AI |
| Feature stores | Feast, Tecton, Hopsworks |
Why T&M works better for ML than fixed-price
ML projects are iterative by nature. You run experiments, the experiments tell you something unexpected, and you adjust the approach. That’s the scientific process working correctly.
Fixed-price ML consulting fights that process. The vendor has an incentive to deliver the originally scoped model, not the model that turns out to be the right one. Scope changes become renegotiations.
T&M billing means the engineers can pivot when the data tells them to. You pay for the actual hours. You don’t pay for hours spent defending a spec that was written before anyone had seen the data.
What the engineers build
Recommendation systems — collaborative filtering, content-based, hybrid approaches. E-commerce, content platforms, internal knowledge tools.
Demand forecasting — time series models (Prophet, ARIMA, LSTM) for inventory planning, workforce scheduling, revenue projection.
NLP and text classification — document classification, entity extraction, sentiment analysis, intent detection. Built on transformer models fine-tuned on your domain data.
Computer vision — object detection, image classification, quality inspection. Usually PyTorch with custom training on your labeled dataset.
LLM fine-tuning — taking an open-source base model (Llama 3, Mistral) and fine-tuning it on your data for a specific task. Cheaper to run in production than calling a hosted API at scale.
What this costs
Indicative monthly team cost for a senior ML engineer:
€5,500 – €8,000/month per engineer, depending on specialization and seniority.
A 2-person team (2 senior ML engineers) runs €11,000 – €16,000/month.
Add an MLOps engineer and you’re looking at €16,000 – €22,000/month for a full team.
Compare that to US-based ML consulting firms, where a senior ML engineer engagement typically runs $25,000–$40,000/month per person, and the work is often done by junior staff.
Related pages
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