AI Governance SME in London

Company Overview

10Pearls is a global, purpose-driven AI-Native digital engineering partner helping businesses re-imagine, digitalize, and accelerate. As an end-to-end digital technology partner, 10Pearls helps businesses create future-proof, transformative digital products that leverage emerging technologies. 10Pearls’ clients include Global 2000 enterprises, high-growth mid-size businesses, and some of the most exciting start-ups from industries like healthcare, fintech, energy, education, real estate, retail, and hi-tech. 10Pearls has product engineering and software development centers in North America, Latin America, Europe, and South Asia, with its London office based in Paddington.

Job Overview

We are seeking an experienced AI Governance SME to design and embed AI governance that lets our clients adopt AI safely and at pace. This is a client-facing role that spans the full governance lifecycle — from enacting drafted frameworks as published, self-serve guardrails placed where teams actually work, through leading the thinking on the AI governance control plane programme, to running the review path for novel and high-risk AI work jointly with client risk and security leaders.

The ideal candidate brings deep expertise in AI governance frameworks and controls, AI security and evaluation, regulatory compliance (ISO 42001, NIST AI RMF, GDPR, EU AI Act), and data governance, combined with the consulting skills to facilitate workshops, influence stakeholders at all levels, and turn policy into guardrails that engineers and citizen developers want to use. This is a unique opportunity to join a global team of over 1,300 product and engineering professionals and play a leading role in growing our AI practices.

Purpose

Turn drafted AI governance framework into governance that runs itself: published, self-serve guardrails that let citizen developers and engineering teams move fast within known limits — with review reserved for the novel and high-risk — and act as the thought leader shaping the AI governance control plane programme. Good governance here is measured by how rarely people have to wait for it. Also interface with existing Data Governance team to champion data domains valuable for AI.

Key responsibilities

• Enact the framework drafted by Risk & CISO as published, self-serve guardrails and patterns — placed where makers actually work. Communicate and evangelize AI Governance policy across the client workforce.
• Support engineers in executing the risk ladder: score each use case once (reversibility, real-world impact, regulatory exposure, auditability) so risk tier decides architecture, testing depth, oversight level and operational monitoring intensity.
• Lead the thinking on the control plane programme: define what good looks like, shape requirements across the IT-led and citizen-led estates, steer the buy/build/extend evaluation and selection, set the policies to enforce first, and stay the design authority through delivery and hand-off into operations.
• Champion data governance for AI data: work with the client’s existing data governance function so the data that is valuable to AI use cases — client data and knowledge included — is supported through their team, within their governance, catalogue and quality systems.
• Run the single review path for novel or high-risk work (new data sources, client data, autonomous agents) jointly with Risk & CISO — every approval becomes a reusable pattern. Maintain guardrails, patterns and best practices library.
• Keep the risk register (models and agents in use) and the firm-wide AI use case register current.
• Work with the client’s existing risk & compliance team to shift AI Governance policies and processes.

Technical skills & experience

• AI governance — frameworks, controls and operating standards, designed for self-service rather than gatekeeping; recognised as a voice worth following on the subject.
• AI security and evaluation depth: prompt injection, jailbreaks, tool misuse; baseline threat evals for agentic systems.
• Risk & compliance: ISO 42001, NIST AI RMF, GDPR and EU AI Act compliance.
• Data governance fluency: classification, cataloguing, quality and access management — enough to partner credibly with a data office.
• Technology selection judgement: shaping requirements, evaluating vendor and build options, and setting enforcement priorities — with policy-as-code and automation as the mechanism.
• Control plane / AI runtime governance tooling: agent identity, real-time policy enforcement, replayable audit, FinOps and shadow-AI discovery.
• Experience embedding governance in a regulated professional-services or similarly complex environment, alongside both pro-code engineering teams and citizen developers.

Soft skills — how this role succeeds

• Communication that lands at every level: explaining risk tiers to a maker in a clinic, evidence readiness to Q&R, and direction to executive sponsors — plainly, without jargon.
• Collaboration over control: this role works inside other people’s teams (Risk, CISO, Q&R, the data office, engineering, platform admins) and wins by making their jobs easier, never by marking their homework — including partnering well with the PM.
• Facilitation and influence: chairing reviews and workshops, building consensus between risk-cautious and delivery-hungry stakeholders, turning pushback into requirements.
• Pragmatic judgement: knowing when a rule protects and when it stifles — comfortable saying yes safely, and saying no with an alternative.
• Empathy for the sceptic: treating concerns as assets, answering each with evidence in the stakeholder’s own workflow.

Working relationships

Reports into the AI Architect and a client-side co-lead. Works day-to-day with risk & compliance and CISO functions (framework owners), quality & risk teams, data governance functions (as champion for AI data), AI guardrails and best-practice engineers (patterns library), AI operations and monitoring leads (monitoring and audit hand-off), and platform administration teams (DLP and environment alignment).