CASE STUDY · STAFF AUGMENTATION FOR AI/ML SPECIALIST ROLES
Scaling a client's engineering team with 6 AI/ML specialists in 3 weeks.
- 6A team of 6 working in week 3
- 8First production AI workflow shipped in week 8
01 · PROJECT OVERVIEW
The client and the brief.
An anonymised mid-market B2B services company. Name withheld under confidentiality.
6 specialists placed (2 AI engineers, 2 ML engineers, a data engineer and a QA engineer): shortlist in 72 hours, first engineer in standup in week 1, all six by week 3.
- 1An AI roadmap stalled for 3 months because in-house hiring had not filled the roles.
- 2The company had clear use cases and executive support.
- 3What it lacked was people who had shipped AI to production, and recruiters were not producing candidates at the pace the plan needed.
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 01In the second week, two engineers waited several days for data access and environment permissions
The client's approval process was built for permanent hires, with several sign-offs. We lost days, not weeks, but it was avoidable. We raised it at the weekly check-in, the client created a fast-track route for the team, and for later engagements we now ask for access requirements during scoping so accounts are ready before day one.
- PROBLEM 02We raised it at the weekly check-in, the client created a fast-track route for the team, and for later engagements we now ask for access requirements during scoping so accounts are ready before day one
03 · THE PROCESS
How we ran it.
- 01Scoping call
We agreed the use cases, the existing stack, the order in which roles were needed and the team lead who would receive the engineers.
- 02Shortlist and interviews
Within 72 hours the client had profiles for each role. Their engineering lead interviewed candidates and chose.
- 03Week 1
The first engineer joined standup and started on the data foundation, so later arrivals had something to build on.
- 04Weeks 2 and 3
The remaining engineers joined in a planned order, each with an onboarding checklist and a named buddy in the client team.
- 05Cadence
Daily standup with the client team, weekly planning, and a short fortnightly check-in with us on fit and progress.
04 · RESULTS
What changed.
Measured against the way the work ran before the engagement began.
- NOW 01
Access and environment setup for new engineers is prepared before they start.
- NOW 02
The team writes an evaluation set before building an AI feature, not after.
- NOW 03
The in-house team has pair-programmed with specialists and carries those practices forward.
- NOW 04
Hiring for permanent AI roles continues, but the roadmap no longer depends on it.
- 6
A team of 6 working in week 3
- 8
First production AI workflow shipped in week 8
Roadmap unblocked
05 · WHAT WE BUILT
How it works.
- The data engineer set up reliable pipelines and quality checks first, since model work depends on them.
- ML and AI engineers built the first production workflow with an evaluation set agreed with the business team before development began.
- The QA engineer wrote test cases for model output and for the surrounding application, so quality was measured before release.
- Work ran in the client's repositories, with their code review rules, CI and environments.
- Model behaviour, latency and cost per request were tracked from the first release.
- Access to data followed the client's least-privilege rules, with named accounts and no shared credentials.
- Controls and handover
- Every engineer documented their work in the client's wiki as they went. Model versions, evaluation results and deployment steps were recorded so the in-house team could run the workflow without us. Production changes needed a reviewer from the client side, and nobody on the augmented team held standing access to production data. Before the engagement settled into a steady state, we ran a handover review with the client's lead to agree which skills they wanted to build internally.
06 · QUICK ANSWERS
The short version.
- What problem did the client have?
- An AI roadmap stalled for 3 months because in-house hiring had not filled the roles. The company had clear use cases and executive support. What it lacked was people who had shipped AI to production, and recruiters were not producing candidates at the pace the plan needed.
- What did Brandsmashers build?
- 6 specialists placed (2 AI engineers, 2 ML engineers, a data engineer and a QA engineer): shortlist in 72 hours, first engineer in standup in week 1, all six by week 3.
- What were the results?
- A team of 6 working in week 3; first production AI workflow shipped in week 8; roadmap unblocked.
YOUR TURN
Have a similar requirement?
Tell us what is stuck. We will come back with a plan and matched, pre-vetted engineers.
