AI Risk & Technical Governance Specialist

This role focuses on the technical governance and assurance of AI systems. It involves operationalizing AI governance requirements throughout the entire AI lifecycle, from initial ideation to retirement. The specialist will review AI and ML systems at a technical level, assessing aspects like model design, data pipelines, validation methods, and monitoring controls. A key part of the role is translating these technical characteristics into clear governance, risk, and regulatory positions, ensuring compliance with frameworks such as the EU AI Act and GDPR. The position also entails conducting risk assessments and assurance reviews for AI systems, identifying potential risks related to bias, explainability, privacy, and robustness. Furthermore, the role requires assessing the adequacy of documentation, controls, and evidence to support internal governance and regulatory scrutiny. The role also involves embedding governance checkpoints into development workflows, designing governance artefacts, and defining minimum evidence requirements for AI systems. Performing technical governance reviews and audits, testing controls, and producing findings and assurance reports are also key responsibilities. The specialist will support audit and regulatory readiness, escalate issues, and track remediation. Additionally, they will help define and track AI governance metrics, develop dashboards, and oversee post-deployment monitoring. The role requires working with data scientists, ML engineers, and product teams to build compliant AI systems without slowing innovation, and building organizational capability through guidance and training. Keeping stakeholders informed on emerging AI regulatory developments and assurance techniques is also part of the role.

• Operationalise AI governance requirements across the full AI lifecycle, from ideation and use-case approval through development, validation, deployment, monitoring, change management, and retirement.
• Review AI and ML systems at a technical level, including model design, feature pipelines, training approaches, validation methods, deployment architecture, monitoring controls, and human oversight mechanisms.
• Translate technical system characteristics into clear governance, risk, and regulatory positions, including mapping to requirements under the EU AI Act, GDPR, LGPD and other relevant frameworks.
• Conduct risk assessments and assurance reviews of AI systems, including risks relating to bias, explainability, privacy, robustness, model drift, misuse, human oversight, transparency, and customer impact.
• Assess whether AI systems have the right documentation, controls, and evidence to support internal governance, regulatory scrutiny, and audit.
• Embed governance checkpoints, review criteria, and control requirements into product, engineering, and model development workflows.
• Design and maintain practical governance artefacts such as AI inventories, use-case classification approaches, risk assessment templates, control libraries, testing standards, and review rubrics.
• Define minimum evidence requirements for AI systems, including documentation of intended purpose, system design, data lineage, validation, performance metrics, limitations, human oversight, and post deployment monitoring.
• Perform technical governance reviews and audits of AI systems and supporting processes.
• Test the design and operating effectiveness of AI-related controls and identify remediation actions where gaps exist.
• Produce structured findings, risk opinions, and assurance reports for technical and non-technical stakeholders.
• Support audit and regulatory readiness by building defensible compliance narratives and evidence packs.
• Escalate material issues, control weaknesses, or ethical concerns and track remediation through to closure.
• Help define and track meaningful AI governance, risk, and assurance metrics.
• Develop dashboards and reporting views that provide visibility of AI use cases, risk status, control coverage, review outcomes, incidents, and remediation progress.
• Oversee or challenge post-deployment monitoring for model performance, fairness, stability, drift, and adverse customer or business outcomes.
• Work directly with data scientists, ML engineers, product teams, and business owners to help them build compliant and trustworthy AI systems without slowing innovation unnecessarily.
• Build organisational capability by translating regulatory and governance expectations into practical guidance, playbooks, and training.
• Keep stakeholders informed on emerging AI regulatory developments, assurance techniques, and good practice.

• Extensive experience across AI/ML, model risk, technical assurance, or technical AI governance, with a meaningful part of that experience in a regulated environment.
• Preferred prior background as an ML engineer, data scientist, model validator, AI architect, or similar technical role, with subsequent and/or experience in AI governance, AI assurance, model risk, or regulatory delivery.
• Experience conducting deep reviews of AI/ML systems beyond policy conformance, including assessment of model design, validation, monitoring, and operational controls.
• Experience translating technical AI concepts into regulatory, risk, audit, or compliance language for senior stakeholders.
• Experience implementing governance processes, controls, and assurance mechanisms in real delivery environments, not just designing high-level frameworks.
• Strong working knowledge of several of the following: EU AI Act, GDPR/LGPD and automated decision-making / profiling considerations, ISO/IEC 42001, ISO/IEC 23894, NIST AI RMF, OECD or other leading responsible AI frameworks, sector or market expectations relevant to gambling, customer protection, and technology risk.
• Strong understanding of how AI and ML systems are built, evaluated, deployed, and monitored in production.
• Ability to interpret model and system documentation, architecture diagrams, data flows, validation results, and monitoring outputs.
• Familiarity with common ML approaches, LLM / generative AI patterns, model lifecycle tooling, and data/ML platforms.
• Familiarity with cloud-based AI environments such as AWS, Azure, or GCP.
• Able to work across technical, product, risk, legal, cybersecurity, and operational teams with credibility.
• Strong written skills, including the ability to produce clear review reports, risk assessments, control evaluations, and executive-ready summaries.
• Comfortable leading workshops, challenging constructively, and driving remediation.
• Able to operate with autonomy and create structure where none exists.
• Strong judgement, pragmatism, and attention to evidence.