About this role
Salary: £70,000 - 75,000 per year
Requirements: 4 to 7 years total experience, including time at a top-tier consulting firm or AI vendorA track record leading end-to-end AI or data science engagements, scoping, sizing value and owning delivery through to adoptionEnough hands-on data science / ML grounding to scope problems credibly, judge feasibility and challenge a technical team, without needing to build full timeStrong change management and stakeholder skills, able to drive adoption across leadership and operational teamsCommercial fluency, comfortable framing AI work in terms of business value, ROI and operational impactConfidence working with senior stakeholders, including in a governance-heavy environmentComfort operating in a lean team, going deep on content rather than just running processExperience across varied industries rather than a single sector is desirablePrivate equity or portfolio value creation experience is desirable Responsibilities: Work with business leadership to identify, size and build the case for high-impact AI use casesPrioritise and sequence use cases, balancing value, feasibility and organisational readinessOwn adoption end to end, including stakeholder engagement, training and change programmes so AI tools become embedded, not shelfwareTrack outcomes against the original business case and ensure deployments translate into measurable impactAct as the bridge between business stakeholders and the technical build team, defining requirements and running eval/regression pipelinesCodify what works into a repeatable playbook the wider team can reuse Technologies: AIEmbedded More:
We are a financial services organisation looking for an AI Deployment Specialist to own the front end and back end of AI adoption across a live business area. You will work alongside a technical build team rather than being part of it, helping us identify and embed AI use cases that deliver measurable value. The role is not hands-on engineering, but we need someone technical enough to define requirements, run evaluation and regression checks in a low-code or no-code way, interpret results and challenge the technical approach without writing code or raising pull requests. A live example of the type of use case in scope is automating a client onboarding process that currently takes circa twenty four months to complete manually, involving document handling, data extraction, policy validation and compliance checks.
last updated 38 week of 2026