Ai Governance London 650

We are hiring a Senior Artificial Intelligence (AI) Governance Specialist/Lead, London-hybrid for 6 months initially. MARKET RATE Inside IR35 EssentialExperience applying AI governance, model risk or responsible AI controls within technology or data-driven programmes. Experience operating within regulated or compliance-sensitive environments. Working knowledge of machine learning pipelines, data governance practices and model monitoring concepts. Experience translating regulatory, policy or risk requirements into practical controls within AI or digital solutions. Ability to engage and influence technical and non-technical stakeholders within cross-functional delivery teams. Desirable Experience developing governance frameworks for emerging technologies such as advanced machine learning or agentic automation systems. Experience operating within multi-supplier or multi-organisation delivery environments. Familiarity with rail industry risk, assurance or safety governance processes. Exposure to model monitoring, life cycle management or Machine Learning Operations practices Responsible AI governance across the by defining and embedding risk management, assurance and compliance controls to support the safe, ethical and regulatory-aligned deployment of AI and automation solutions. Define and embed AI governance principles, risk controls and assurance frameworks across the programme to support consistent and responsible AI deployment Review and challenge AI and automation solutions against regulatory expectations, including data, security, explainability, bias mitigation and safe operation Identify, assess and manage risks specific to AI systems, including model reliability, agentic behaviour constraints, data usage and ethical considerations. Direct the development and maintenance of comprehensive governance artefacts including model risk assessments, data handling controls, audit mechanisms, monitoring frameworks and exception management processes. Shape governance approaches for machine learning and agentic automation systems to support responsible experimentation and controlled deployment. Work in close partnership and collaborate with the AI Solutions Architect, Data Scientists and Engineers to embed governance controls throughout the solution life cycle. Translate policy, regulatory and risk requirements into practical technical controls and guardrails within AI solution designs. Define and oversee performance metrics, key risk indicators and monitoring approaches to identify model drift, governance breaches and emerging risk patterns.