AI Deployment Specialist - London

Salary: £70,000 - 75,000 per year

Requirements

• 4 to 7 years total experience, including time at a top-tier consulting firm or AI vendor
• A track record leading end-to-end AI or data science engagements, scoping, sizing value and owning delivery through to adoption
• Enough hands-on data science / ML grounding to scope problems credibly, judge feasibility and challenge a technical team, without needing to build full time
• Strong change management and stakeholder skills, able to drive adoption across leadership and operational teams
• Commercial fluency, comfortable framing AI work in terms of business value, ROI and operational impact
• Confidence working with senior stakeholders, including in a governance-heavy environment
• Comfort operating in a lean team, going deep on content rather than just running process
• Experience across varied industries rather than a single sector is desirable
• Private 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 cases
• Prioritise and sequence use cases, balancing value, feasibility and organisational readiness
• Own adoption end to end, including stakeholder engagement, training and change programmes so AI tools become embedded, not shelfware
• Track outcomes against the original business case and ensure deployments translate into measurable impact
• Act as the bridge between business stakeholders and the technical build team, defining requirements and running eval/regression pipelines
• Codify what works into a repeatable playbook the wider team can reuse

Technologies

• AI
• Embedded

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