Senior Manager, Responsible AI

About the AI Acceleration Hub

Irish Life is accelerating its use of Artificial Intelligence to deliver better customer outcomes, improve operational effectiveness, unlock new sources of value, and strengthen the organisation’s competitive capability. The AI Acceleration Hub plays a central role in enabling this ambition by supporting the identification, development, validation and productionisation of AI-enabled solutions across the Group.

As AI adoption increases, it is critical that solutions are developed and deployed in a controlled, transparent, explainable, secure and responsible manner. This requires more than policy and governance oversight alone. It requires a practical technical Responsible AI capability that can be embedded into how AI applications are assessed, designed, tested, released, monitored, changed and evidenced in live operation.

The AI Acceleration Hub is therefore establishing a Responsible AI technical capability that will help delivery teams move faster and more safely through reusable patterns, tooling-enabled controls, evidence standards, evaluation approaches, runtime guardrails and monitoring practices. The capability will work alongside existing governance, risk, architecture, legal, compliance, privacy and security processes, translating Responsible AI expectations into practical delivery controls and engineering practices.

The Job

Reporting to the Head of Enablement Layer, AI Acceleration Hub, the Senior Manager, Responsible AI will lead the technical side of Irish Life’s Responsible AI framework for Hub-led and Hub-enabled AI solutions. The role will be accountable for shaping, implementing and continuously improving the technical Responsible AI capability within which AI solutions should operate.

Working closely with the AI Governance Lead and relevant control functions, the role will translate Responsible AI principles, regulatory expectations, risk requirements and governance standards into practical technical controls, reusable delivery patterns, evaluation frameworks, evidence models, monitoring approaches and revalidation processes. This is not intended to duplicate or replace existing AI governance ownership.

The role will help define how AI applications move safely from idea through design, build, testing, approval, production release, live monitoring and material change. It will provide technical leadership across Responsible AI tooling and implementation patterns, including AI evaluation, model and prompt control, retrieval and grounding controls, runtime guardrails, telemetry, traceability, evidence capture, monitoring, exception handling and production supportability.

The successful candidate will work through and with delivery squads, platform teams, architecture, engineering, data, risk, compliance, legal, data protection, security, audit, procurement and external partners. They will not need to personally build every control, but they must have enough technical depth to understand what good looks like, challenge implementation choices, guide delivery teams, shape tooling decisions and ensure Responsible AI controls are implemented in a way that is practical, scalable and credible.

Over time, the role will build and lead a small specialist Responsible AI technical capability, expected to grow to approximately two to three people, providing hands-on enablement, technical assurance, reusable patterns and delivery support to AI squads across the organisation.

Key Accountabilities

•    Lead the technical Responsible AI framework – Own and evolve the technical framework, translating Responsible AI principles and governance requirements into practical controls, patterns, tooling and evidence standards.
•    Build the shared Responsible AI capability – Design and scale shared Responsible AI capabilities spanning control configuration, evaluation, guardrails, monitoring, evidence capture and revalidation.
•    Embed Responsible AI into AI delivery and productionisation – Integrate Responsible AI requirements throughout the AI lifecycle, from initial assessment and design through testing, approval, deployment, monitoring and change.
•    Define technical control patterns and standards – Establish practical AI architecture and control standards covering models, prompts, grounding, permissions, data, observability, human oversight, resilience and supportability.
•    Lead AI evaluation, testing and revalidation approaches – Define proportionate approaches for AI evaluation, adversarial testing, outcome validation, expert review, threshold setting and material-change revalidation.
•    Implement runtime guardrails and policy enforcement patterns – Define appropriate runtime controls including policy checks, prompt and grounding protections, warning or blocking mechanisms, escalation and exception handling.
•    Drive telemetry, evidence capture and Responsible AI observability – Define logging, traceability, monitoring and evidence requirements supporting live oversight, assurance, auditability, incident response and ongoing review.
•    Shape platform and tooling direction – Provide technical leadership on Responsible AI tooling across approved AI platforms, ensuring platform controls align with Irish Life control outcomes.
•    Support AI squads and delivery teams – Advise and enable AI squads to implement required controls, evidence and safer AI patterns and navigate review and release processes effectively.
•    Partner with AI Governance and control functions – Translate governance, risk, compliance, legal, privacy, security and architecture expectations into practical technical implementation approaches.
•    Establish governance-connected delivery pathways – Integrate Responsible AI technical checks, evidence, approvals, exceptions and material-change triggers into existing governance and delivery processes.
•    Support third-party and vendor alignment – Assess AI-enabled products and partners, ensuring vendor controls, data handling, model behaviour and monitoring align with Irish Life Responsible AI expectations.
•    Build and lead a small specialist team – Build and lead the Responsible AI technical capability, providing technical direction, coaching, prioritisation, quality assurance and delivery support.
•    Measure and communicate Responsible AI technical maturity – Develop measures covering control adoption, evidence quality, testing, monitoring, implementation gaps and tooling maturity across the AI portfolio.
•    Champion responsible technical innovation – Promote AI innovation through clear technical standards, proportionate controls, transparency, accountability, human oversight, customer focus and operational resilience

The Person

The successful candidate will be a senior, technically credible leader with strong experience in AI, data, technology delivery, architecture, engineering governance, digital transformation, technical risk, assurance or regulated financial services. They will be able to operate confidently across strategic, technical and governance conversations, translating Responsible AI expectations into clear delivery requirements and practical implementation decisions.

They will bring a strong understanding of how AI-enabled solutions are designed, built, tested, deployed and operated in a business environment. They do not need to be a deep specialist in every emerging AI tool, but they must be sufficiently technically fluent to guide teams, challenge design assumptions, assess platform controls, interpret evaluation evidence and understand the implications of model, prompt, retrieval, data, workflow or vendor changes.

The role requires someone pragmatic, collaborative and outcome-focused, with the judgement to balance innovation, speed of delivery, customer outcomes, regulatory expectations and operational risk. They must be comfortable working in an emerging discipline where standards, tooling and regulatory expectations are still developing, and where success will depend on building scalable patterns rather than relying on bespoke review for every use case.

The ideal candidate will be able to build trust across business, technology and control teams, while also developing a small specialist Responsible AI technical team that can support multiple AI squads and help mature the organisation’s Responsible AI capability over time.

Experience and Skills Required

•    Strong experience across AI, data, technology delivery, architecture, technical assurance, risk, compliance or regulated financial services, working effectively with technical and business stakeholders.
•    Practical understanding of AI and machine learning, including Generative AI, LLMs, retrieval-augmented generation, agents, model validation, explainability, bias, human oversight and operational controls.
•    Ability to translate Responsible AI principles, regulatory expectations and risk requirements into technical controls, evidence standards and delivery practices.
•    Understanding of AI solution architecture, including orchestration, APIs, cloud AI services, data flows, retrieval, permissions, system integration and production supportability.
•    Experience with, or ability to develop competence in, platforms and tools such as Azure AI Foundry, Azure OpenAI, Snowflake Cortex, evaluation, observability and AI guardrails.
•    Experience applying technical controls across the AI delivery lifecycle, including release management, versioning, configuration control, change management and material-change revalidation.
•    Ability to shape AI evaluation and testing, including adversarial testing, expert review, thresholds, quality measurement, grounding checks and repeatable evidence for probabilistic outcomes.
•    Strong understanding of telemetry, logging, monitoring and evidence capture for live oversight, auditability, incident response and ongoing assurance.
•    Ability to assess runtime guardrails, including content filtering, prompt protection, access controls, warning or blocking logic, escalation, fallback and exception handling.
•    Experience working across technology, data, security, privacy, risk, legal, compliance, procurement, audit and business teams to deliver controlled outcomes.
•    Strong stakeholder management and influencing skills, with the ability to explain complex AI, technical and control topics clearly to senior leaders and delivery teams.
•    Experience preparing senior-level materials, technical evidence, architecture papers, governance submissions, approval packs and assurance documentation.
•    Strong planning and organisational skills, managing competing priorities and establishing repeatable delivery patterns within an evolving environment.
•    Experience building, leading and coaching a small specialist team, providing direction, prioritisation and capability development.
•    Growth mindset and strong interest in emerging AI technologies, Responsible AI tooling, regulatory developments, operational resilience and responsible innovation.