Your roadmap has an AI feature on it, and nobody on the team has built one. Hiring a senior machine learning engineer locally is often the first idea and the hardest to fund. There is another route: two people, with the right split of work, in your satellite office.
Start with the data, not the model
Most AI features fail for a dull reason: the data underneath them is not ready. Before anyone trains, prompts or fine-tunes anything, someone has to:
- get the right data in one place
- clean it
- keep it flowing reliably
That is a data engineer’s job. It comes first.
What a data engineer owns
- Junior builds and maintains pipelines into the warehouse, monitors their health, and documents sources and changes.
- Mid designs pipelines end to end, from ingestion to delivery. They own warehouse structure and documentation, and handle monitoring and recovery.
- Senior owns the warehouse architecture, the pipeline platform, data quality and governance.
What an AI/ML engineer owns
- Junior trains and evaluates models, builds preprocessing pipelines, and runs and documents experiments.
- Mid owns the full machine learning lifecycle for a feature through to production. They build experiment tracking and retraining pipelines, and apply LLM APIs and fine-tuning to real business problems.
- Senior decides which problems to tackle and whether to build or buy. They design production-grade architecture and set the technical bar for AI work.
Which combination fits
| Your situation | A sensible starting pair |
|---|---|
| You know the feature; the data is scattered | Mid data engineer, then a mid AI/ML engineer |
| The data is in good shape; you need the feature built | Mid AI/ML engineer |
| Nobody on your team can judge the technical approach | A senior AI/ML engineer, because someone has to own the decisions |
What it costs
| Seat | Junior | Mid | Senior |
|---|---|---|---|
| Data engineer | $2,200 to $3,000 | $3,000 to $4,500 | $4,500 to $6,500 |
| AI/ML engineer | $2,500 to $3,500 | $3,500 to $5,000 | $5,000 to $7,500 |
All prices are per month and all-inclusive, with $0 until you hire.
What stays yours
- The product decision: which problem the feature solves, and for whom.
- Judging whether it works for your customers.
The people in your satellite office build it alongside your team, and you manage the work.
See the scenario in add AI capacity without a senior hire, the AI/ML engineer role on /hire/ai-ml-engineer, or talk to us.