CASE STUDY · STAFF AUGMENTATION FOR AI/ML SPECIALIST ROLES
Embedding an ML engineer into an existing team without losing momentum.
- 2→6 aReleases up from 2 to 6 a month
01 · PROJECT OVERVIEW
The client and the brief.
An anonymised mid-market B2B services company. Name withheld under confidentiality.
A senior ML engineer paired with the team lead, working in the client's tools and processes, with documented handover. The engineer built the forecasting feature alongside the team's existing work.
- 1The operations team needed ML skills mid-project without slowing the roadmap.
- 2A new project needed a demand-forecasting capability, but the team was already shipping against commitments.
- 3Pausing to recruit, or to retrain existing staff, would have pushed dates.
02 · WHAT WENT WRONG
What went wrong, and how we fixed it.
Every engagement hits problems. These were the ones that mattered here, and what we changed so they did not come back.
- PROBLEM 01The first model scored well on the evaluation set but performed poorly in a shadow run against live data
A data feed had changed its definition of a field, so the training data and live inputs differed. We added a schema and distribution check at the start of the pipeline, alerts when inputs drifted, and a short data contract with the owning team. The feature then went live with monitoring that would have caught the problem.
- PROBLEM 02The feature then went live with monitoring that would have caught the problem
03 · THE PROCESS
How we ran it.
- 01Scoping
We described the problem, the data available and the team's definition of done. The client interviewed one candidate and approved.
- 02First week
The engineer joined standup, read the codebase, and paired with the team lead on a small ticket to learn conventions.
- 03Next phases
Baseline model and evaluation first, then a first version behind a flag, then production monitoring.
- 04Cadence
Daily standup, weekly planning, and a short written update each Friday on progress, risks and decisions.
- 05Handover
The engineer documented the model, the data and the monitoring as they went, and ran sessions for the team.
04 · RESULTS
What changed.
Measured against the way the work ran before the engagement began.
- NOW 01
The team treats evaluation and monitoring as part of the feature, not an afterthought.
- NOW 02
Data changes are communicated through a simple data contract.
- NOW 03
Team members can retrain and check the forecasting model without outside help.
- NOW 04
Release frequency stays higher because the ML work fits the normal delivery process.
- 2→6 a
Releases up from 2 to 6 a month
A monitored demand-forecasting feature in production
No roadmap slippage
05 · WHAT WE BUILT
How it works.
- A simple baseline, so improvement could be measured against something real rather than against a feeling.
- Feature engineering from the company's operational data, with data-quality checks at the start of the pipeline.
- Evaluation on a held-out period that reflected how forecasts would be used, not a random split.
- Deployment through the client's existing release process, behind a feature flag.
- Monitoring for forecast error and input drift, with alerts to the team.
- Retraining steps documented and scripted, so the team could rerun them.
- Controls and handover
- The forecasting feature ships with a short model card: what data it uses, how it was evaluated, known weaknesses and when it should be retrained. The operations team agreed an owner for the monitoring alerts and a rule for when to fall back to the previous method. At the end of the engagement the engineer ran two walk-through sessions and left a runbook for retraining and for investigating a bad forecast.
06 · QUICK ANSWERS
The short version.
- What problem did the client have?
- The operations team needed ML skills mid-project without slowing the roadmap. A new project needed a demand-forecasting capability, but the team was already shipping against commitments. Pausing to recruit, or to retrain existing staff, would have pushed dates.
- What did Brandsmashers build?
- A senior ML engineer paired with the team lead, working in the client's tools and processes, with documented handover. The engineer built the forecasting feature alongside the team's existing work.
- What were the results?
- Releases up from 2 to 6 a month; a monitored demand-forecasting feature in production; no roadmap slippage.
YOUR TURN
Have a similar requirement?
Tell us what is stuck. We will come back with a plan and matched, pre-vetted engineers.
