Hire engineers
Hire Python engineers for backends, data and forecasting
These engineers write the services and data pipelines behind a product, and the classic machine-learning models that sit on that data. Language-model systems are a separate role.
What these engineers do
- Build backend services and APIs that other systems call.
- Build data pipelines that load, check and shape data into a warehouse.
- Train forecasting models for demand and revenue, and scoring models for risk and propensity.
- Detect anomalies in transactions and sensor readings.
- Plan routes, prices, schedules and stock levels with optimisation methods.
- Watch live models for drift and retrain them.
- Put data-quality checks in each pipeline, so a bad load stops before it reaches a report.
Stack
- Language and models
- Python, XGBoost and deep learning, with feature stores
- Serving
- streaming inference
- Warehouse
- PostgreSQL, ClickHouse or BigQuery
- Pipelines
- dbt, Airflow or Dagster, and Kafka
- Tests
- PyTest, run on every change
Your warehouse and scheduler come first. We add to what you run before we propose another tool.
The kind of work
Machine-learning engineers on a lending build produced the risk scorecards. Data pipelines fed them, and each score could be explained afterwards. See the case.
On a forecasting engagement, the handover is a model that beats a baseline, its feature pipeline, drift monitoring and a runbook for retraining.
Who this role fits
Hire these engineers when data sits in several places and nobody owns the pipeline, when a forecast still lives in a spreadsheet, or when a product team needs a backend built in Python. For assistants, document readers and agents on language models, use the AI engineers role.
What to put in your brief
- The data sources, their size and how often they change
- The decision a forecast or score should support
- Where results must appear: a dashboard, an API, a screen
- Your current warehouse and scheduler, if any
- Whether the engineer joins a team or owns a pipeline
How a team starts
You send a brief with the stack, the data and the outcome you want. We shortlist, you interview and you approve each engineer, and they join your repositories and ticketing. A single engineer is the smallest start, with a one-month minimum and 30 days' notice. Hire terms apply to every role. How soon engineers start is agreed at the start of the engagement.
Tell us the role
What data do you have, and what should it predict?
Tell us what you want to build.
The first call is 45 minutes with an architect, usually the founder. It is free, and we will sign an NDA first if you ask.