Real anonymised engagement · Case study

Building AI/ML Capability for a Multinational Enterprise

Captivolt helped define the operating model, role architecture, capability framework, hiring validation, and ramp approach required to build repeatable AI/ML capability.

A multinational enterprise can now define, validate and grow AI/ML capability the same way in every team, using its own people.

Engagement evidence

What the work produced

Capability model
Role architecture
Validation scorecards
Onboarding & ramp system

CLIENT CONTEXT

A multinational enterprise needed to move beyond isolated AI experiments to a repeatable, in-house AI/ML engineering capability.

BUSINESS PROBLEM

Roles, skills, validation standards, and ramp practices varied by team, so capability could not be hired or grown consistently.

CONSTRAINTS

Limited internal AI/ML expertise
Capability split across teams and geographies
No shared definition of what an AI/ML role is
Hiring already under way and unable to pause

ARCHITECTURE & APPROACH

Defined the operating model, role architecture, and capability framework; designed hiring validation and structured onboarding and ramp approaches.

WHAT CAPTIVOLT DELIVERED

Operating model · role architecture · capability framework · hiring validation design · onboarding and ramp model.

EVIDENCE

Anonymised artefacts and reference discussion available under NDA where client permission allows.

The public case study describes the system and the work. It does not publish the client’s identity, source data, commercial information or unapproved performance figures.

In detail

How the capability system fits together.

The capability architecture

What was built, and in the order that made the rest possible.

Role architecture: Job families defined by the work rather than by the job advert: AI and ML engineering, data, platform, quality and security named as distinct families with distinct assessment.
Competency model: Three or four dimensions per family, chosen because the work fails without them, with the evidence a level requires stated as something a person has done.
Level mapping: Mapped onto the group’s existing ladder rather than replacing it. A parallel ladder is an HR problem nobody asked for, and the reason most frameworks are never used twice.
Capability model: Which roles the roadmap demands, at what depth, against which systems: demand-side, so it changes when the roadmap does.

Assessment artefacts

The things a hiring manager or a lead actually picks up and uses.

Validation scorecards: Per family and per level, recording what a candidate did rather than how they described it, with a verdict per dimension instead of a composite number.
Structured exercises: Problems shaped like the work, with incomplete information, worked in front of somebody.
Interview and assessment kit: So two assessors in two geographies reach comparable conclusions about comparable people.
Decline reasons: Recorded in the candidate’s file rather than in a thread, because the same candidate is often seen again.

The onboarding model

Ramp time is the real cost in an organisation hiring continuously. The onboarding system is an economic decision.

Day-one environment: Access, tooling and a working local setup on the first day rather than in the second week.
Practice tasks against real systems: Scoped work on the actual estate, not a sandbox that teaches the sandbox.
Ramp scorecard: Checkpoints that say whether the ramp is working, early enough to change it for the next joiner.
Named owner per joiner: Someone accountable for the ramp, distinct from the manager who is accountable for the delivery.

Publication boundary

What is not published here.

Scale and outcome evidence (how many people were assessed, across how many teams and geographies, and what happened to ramp time) are the client’s figures. None has been approved for publication, so none is shown. The structure that would carry them is above, and a number will appear when the client agrees to it rather than before.

OUTCOME

The organisation gained a consistent, documented model for defining, validating, and growing AI/ML capability across teams.

WHAT THE CLIENT OWNS NOW

The operating model and role architecture
The capability framework
Hiring validation scorecards
The onboarding and ramp system

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Anonymised artefacts and reference discussions are available under NDA where client permission allows.

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