Contracting

Engineering capacity on contract, alongside a team that already exists or as the resource a project is short of. By the day or by the engagement, set out in writing the same way a fixed-scope build is.

Ask about contracting

Where it usually starts

Most often a model that works in a notebook and has stalled on the way to production, or a delivery date that needs a second pair of hands rather than a second opinion. Both are capacity problems, and both are quoted by time rather than by scope.

What the work looks like

The engineering underneath an AI system is most of the job. The model is rarely the hard part.

Prototype to production

  • Deployment and rollback
  • What happens when it is wrong
  • Cost of running it, before it surprises anyone

The data underneath

  • Ingest, transform, schedule
  • How outputs are stored and audited
  • Reproducible rather than one-off

Instrumentation

  • Logging that answers a question later
  • Metrics chosen before the incident
  • Alerting that is worth waking up for

How it is arranged

Capacity, not a project. The scope stays with whoever owns the roadmap.

Fitting in

  • Works to the conventions already in place
  • Uses the existing board, repo and review process
  • No separate tooling imported

Terms

  • By the day or by the engagement
  • Agreed in writing before it starts
  • Handover documented, not held in someone's head

What it is grounded in

Ten years building and running AI and machine-learning systems, a Masters in Computer Science, and delivery experience across ecommerce, industrial equipment and professional services — which means arriving already familiar with how a delivery team works, rather than learning it on the engagement.

Ask about contracting

What the project is, what it is short of, and roughly how long for. That is usually enough to say whether it is a fit.

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