AI Portfolio Architect

Role Overview

The AI Portfolio Architect owns the enterprise gradeAI strategy, architecture, and infrastructure for projects across Customer Solutions (CS), ensuring all AI solutions are designed, delivered, and operated in alignment with ILG’s AI Governance Framework, the EU AI Act, GDPR, Central Bank of Ireland expectations, EIOPA guidance, and emerging European regulatory standards.

The role ensures all AI architectures are compliant, secure, explainable, resilient, and effectively governed, supporting the AI Governance Lead through the translation of policy and regulatory requirements into actionable architectural controls and technical patterns.

The successful candidate will partner closely with the AI Governance Lead, fellow Portfolio Architects, Solution Architects, Engineering, Risk, Data, EXO, European governance bodies, and business stakeholders to deliver safe, ethical, and scalable AI across Irish Life Group.

The Role

As an AI Portfolio Architect, you will:

Strategic Leadership & Direction

•  Define and champion the long term strategic vision for AI within Customer Solutions and across ILG.
• Develop and maintain an enterprise aligned roadmap for safe, scalable AI adoption.
• Shape long term capability development across AI platforms, lifecycle maturity, governance, and engineering.
• Advise senior leaders on AI investment decisions, platform evolution, and capital planning.
• Identify strategic AI opportunities and threats, informing organisational direction and risk posture.

Define & Evolve AI Architecture

• Define and continuously enhance enterprise AI architecture, reference patterns, and guardrails across analytics, ML, and generative AI.
• Translate AI governance requirements—including EU AI Act obligations, GDPR, DORA, Irish regulatory guidance, and ILG governance rules—into actionable architectural controls and technical standards.
• Ensure the architecture supports transparency, safety, explainability, auditability, and alignment with ILG’s enterprise architecture direction.

Embed AI Governance & Regulatory Compliance

• Implement governance frameworks defined by the AI Governance Lead by embedding required architectural controls into solution designs (e.g., bias mitigation, explainability, hallucination control, HITL, model monitoring, lifecycle management).
• Provide technical risk assessments, architectural artefacts, and evidence packs in support of the Central AI Register, high risk use case classification, and regulatory or audit reviews.
• Ensure CS AI solutions are designed to meet European governance expectations and support escalations to EU Governing Committees where required.
• Implement robust Risk Management practices by identifying potential technical risks and architectural challenges within the CS AI domain, and by proposing mitigation strategies to ensure system stability, scalability, and optimal performance.

Shape the AI Use Case Portfolio

• Partner with Product and Portfolio leadership to shape a prioritised AI opportunity pipeline.
• Assess technical feasibility, architectural risk, governance implications, and regulatory classification early in the use case lifecycle.
• Provide architectural leadership across discovery, design, delivery, and operationalisation.

Cloud Solutions, Governance Forums & Collaboration

Lead Architecture of Azure AI & Cloud Native Solutions

• Develop the CS AI Roadmap, outlining future strategic directions aligned with the business strategy.
• Provide leadership and direction to the CSIT teams as well as supporting business teams.
• Lead the design of Azure AI and related cloud native solutions, ensuring scalability, robustness, performance, and compliance with ILG security and risk standards.
• Promote and embed mature MLOps/LLMOps operating models and integrated AI lifecycle controls.
• Foster proactive Stakeholder Engagement by routinely liaising with business leaders, vendors, and fellow architects to comprehend business needs and effectively communicate the technical possibilities and limitations within the Customer Solutions and Irish Life Group frameworks.

Contribute to Governance Forums

• Be a standing member in the Customer Solutions Design Authority (CSDA) and represent the portfolio in Irish Life Group Enterprise Architecture Board & Forum, ensuring decisions align to ILG and enterprise frameworks.
• Represent AI portfolio interests at ILG Architecture & AI Governance Boards, working closely with the Head of Strategy & Architecture, Risk Leads, ILG AI Governance Lead and wider ILG community.
• Ensure all AI designs meet ILG enterprise architecture principles, security requirements, and regulatory obligations.
• Be an integral member of the Irish Life Group Architecture Community, working closely with peers across the Irish Life Group and engaging with the ILG Head of Strategy & Architecture to ensure alignment of architectural principles and strategic direction.

Cross Functional Collaboration

• Partner with the AI Governance Lead, Risk, Legal, Data, EXO, Engineering, and operational teams to ensure safe and compliant delivery of AI.
• Translate complex regulatory and technical requirements into clear, business friendly guidance.
• Support the uplift of AI literacy, governance capability, and good architectural practice across CS and ILG.
• Exercise Commercial Awareness by Design, ensuring that all team members are driven by business value and fully consider the Total Cost of Ownership (TCO) in all design decisions.

Continuous Improvement & Innovation

• Stay ahead of emerging AI regulations, industry standards, model safety techniques, and architectural innovations to optimally leverage new and existing technology for the business.

Key Skills and Capabilities

• Excellent collaboration skills, essential for working within your team and engaging with peers across the IS organisation, particularly data science, engineering, and AI governance groups.
• Ability to influence and provide effective support to Senior IS and Business leaders, ensuring AI-driven initiatives align with strategic objectives and responsible AI (RAI) principles.
• Proficient in partnering with senior management both within Irish Life and across the wider group, enabling the organisation to make informed investment decisions related to AI platforms, capabilities, and automation opportunities.
• Proven track record of delivering high‑quality, complex AI and data‑driven solutions within a large enterprise environment, including experience leading architectural direction for analytics, machine learning, and enterprise AI adoption.
• Good knowledge of Application Engineering, complemented by experience with MLOps, AI lifecycle management, and Agile delivery practices, ensuring AI models and intelligent services are robust, scalable, and continuously improved.
• Capable of producing high‑quality architectural and design documentation, including AI reference architectures, data flow models, and AI governance artefacts, along with delivering insightful presentations to senior management and project teams.
• Excellent problem‑solving and decision‑making abilities, with an aptitude for assessing AI‑related risks, technical trade‑offs, ethical and other considerations.
• Experience in designing AI‑enabled solutions that drive business value and address complex issues, leveraging machine learning, generative AI, predictive analytics, and automation frameworks.
• Significant experience with data integration patterns, AI security best practices, and model governance frameworks, including handling sensitive data securely and ensuring models meet compliance and regulatory obligations.
• Significant experience with integration patterns, security best practices, and applications development environments.
• Versatile experience working with various deployment models, including On‑Premise, Private Cloud, Hybrid Cloud, and Public Cloud AI services, ensuring seamless alignment across the organisation’s technology estate.
• Knowledge of modern AI architecture patterns and enabling technologies (e.g., vector databases, LLM orchestration frameworks, prompt engineering, model evaluation techniques)
• Experience with the Microsoft AI ecosystem, including Azure OpenAI, Cognitive Services, Fabric, and the Power Platform, enabling enterprise‑grade AI workloads and business‑led innovation.
• Experience writing scripts and automation workflows using Python, Powershell, or similar languages, including integration with APIs, AI pipelines, and DevOps/MLOps tooling.
• Understanding of data and model infrastructure concepts, such as feature stores, model registries, inference optimisation, and scalable API deployment patterns.
• Skilled in aligning non‑functional requirements of AI solutions—including fairness, interpretability, performance, latency, scalability, and cost—with the organisation’s business strategy and risk appetite.
• Strong understanding of emerging AI regulations, particularly the EU AI Act, with the ability to interpret legal and regulatory text and translate it into practical technical and architectural requirements for AI systems. This includes familiarity with risk‑tier classification, conformity assessment, data quality obligations, transparency requirements, and human oversight mechanisms.
• Ability to embed regulatory compliance into architecture and delivery, ensuring that AI solutions meet mandatory logging, monitoring, robustness, and documentation requirements and that teams understand their responsibilities under the Act.

Essential Requirements

• Minimum of 7 years of experience in the technical design and delivery of strategic AI, data, or analytics‑driven solutions, including exposure to enterprise‑scale platforms, automation frameworks, and model‑driven architectures.
• Strong understanding of modern AI integration and orchestration services, such as Azure Machine Learning, Azure OpenAI, Cognitive Services, Logic Apps, Azure Functions, and enterprise API layers that support intelligent workflows, retrieval‑augmented generation (RAG), and end‑to‑end AI solution delivery.
• Advanced knowledge of AI lifecycle automation, including MLOps practices, model evaluation pipelines, automated deployment strategies, and AI risk management controls, ensuring responsible and consistent rollout of AI capabilities across environments.
• Experience of AI security, scalability, observability, and performance optimisation, with the ability to evaluate model performance, system throughput, inference latency, and architectural trade‑offs in highly regulated environments.
• Strong understanding of best practices in Enterprise Architecture, with the ability to embed AI‑specific governance standards, architectural patterns, and compliance requirements across the organisation.
• Familiarity with Agile methodologies and collaborative team practices, particularly within cross‑functional teams involving data engineers, AI engineers, product owners, and business stakeholders.
• Strong stakeholder management and communication skills, necessary for articulating complex AI concepts, model behaviour, risks, and technical constraints in accessible and meaningful ways.
• Practical experience integrating regulatory and compliance frameworks into AI delivery, including the capability to operationalize EU AI Act requirements across design, development, deployment, and ongoing oversight of AI systems.
• Demonstrated ability to collaborate with Legal, Compliance, Risk, and Governance teams to ensure enterprise‑wide alignment on AI Act obligations and to support audits, conformity assessments, and regulatory reporting where required
• Sound understanding of current Application, Infrastructure, and Security Frameworks.

Desirable Skills

• A deep understanding of Microsoft Azure AI and data technologies, including Azure Machine Learning, Azure OpenAI, Cognitive Search, Databricks on Azure, Fabric, and related services, with the capability to rapidly learn emerging tools and assess relevant AI trends.
• Experience with end‑to‑end AI solution architectures across IaaS, PaaS, and SaaS models, including model training, deployment, optimisation, monitoring, and drift detection within enterprise environments.
• Experience working in the financial services sector, preferably in life assurance, where AI governance, ethical considerations, and compliance requirements carry heightened significance.
• Hold certification(s) in relevant AI or cloud architecture domains, such as Azure Solutions Architect Expert, Azure AI Engineer Associate, or other industry‑recognised AI/ML credentials.
• Knowledge of Regulatory requirements (EU AI Act/GDPR etc) including implementation patterns, best practice approaches for technical documentation, risk management systems, post market monitoring, and human oversight design.
• Experience developing or contributing to AI assurance frameworks, including model cards, system documentation templates, transparency artefacts, and audit‑ready controls aligned to regulatory standards.