About the position

The AI Architect will help shape and govern AXIS Capital’s AI landscape by translating business opportunities into practical technical designs. Working across architecture, engineering, data, security, risk and business teams, the role helps ensure AI capabilities are adopted consistently, securely and responsibly, and aligned with business outcomes. The role designs enterprise-grade AI architectures across generative AI, machine learning, intelligent automation and agent-based use cases. It establishes reusable architecture patterns, shared components, standards and guardrails; maintains oversight across multiple initiatives; and supports teams throughout implementation. The AI Architect also contributes to governance and design assurance, evaluates emerging technologies and vendors, and helps stakeholders identify where AI can create measurable value. This position reports into the appropriate Architecture leadership function. AXIS offers a hybrid work model. The expected base salary range is \$80,000–\$100,000, with target incentive compensation based on corporate and individual performance. The role is an existing vacancy.

Responsibilities

• Design secure, scalable and resilient enterprise-grade AI architectures for generative AI, machine learning, intelligent automation and agent-based use cases.
• Translate business needs into AI designs and explain technical choices, benefits, limitations, risks and trade-offs to technical and non-technical stakeholders.
• Define and promote reusable AI architecture patterns, shared components, standards and guardrails for model selection, orchestration, grounding, integration, observability and human oversight.
• Identify common requirements across AI initiatives and develop reusable components, services and reference implementations.
• Maintain architectural oversight across AI projects, identify duplication and guide teams toward shared capabilities.
• Guide teams on responsible AI practices covering fairness, explainability, transparency, privacy, security, safety, regulatory compliance and human control.
• Assess AI use cases for suitability and recommend simpler alternatives where they offer better value, control or maintainability.
• Architect secure AI integrations with enterprise data, applications, APIs and workflow platforms, working with Data, Integration, Cloud and Security Architecture.
• Support solution teams with retrieval-augmented generation, semantic search, prompt and context management, model evaluation, MLOps and LLMOps approaches.
• Ensure designs address testing, monitoring, cost management, performance, resilience, auditability and lifecycle controls.
• Contribute to AI governance and design assurance, present proposals to architecture and risk review forums, and track key decisions.
• Evaluate emerging AI technologies and vendors through structured assessments and proofs of concept, balancing innovation with operational and regulatory risk.
• Collaborate with engineers and delivery teams during implementation, resolving design issues while maintaining alignment with enterprise standards.
• Build relationships across business and technology teams, facilitate workshops and help stakeholders understand where AI can create measurable value.
• Maintain awareness of AI technology, industry practice and regulation and use that knowledge to evolve architecture guidance.
• Take on additional duties, responsibilities and activities appropriate to the nature of the role.

Requirements

• At least 5 years of relevant experience in AI, machine learning, data science, software engineering, cloud architecture or solution architecture, including designing or delivering AI-enabled solutions.
• Strong technical background and practical understanding of software engineering, cloud platforms, APIs, data integration, security and modern development practices.
• Experience building or designing solutions across Databricks and Azure platforms.
• Proven experience designing enterprise-grade AI architectures that meet security, scalability, resilience, performance, observability, integration, governance and cost-control requirements.
• Working knowledge of generative AI and machine learning concepts, including large language models, embeddings, vector search, retrieval-augmented generation, agents, model evaluation and prompt design.
• Experience designing solutions on a major cloud platform.
• Understanding of AI delivery and operational practices, including MLOps or LLMOps, versioning, testing, deployment, monitoring, observability and lifecycle management.
• Knowledge of responsible AI principles and ability to apply privacy, security, fairness, explainability, transparency, safety and human-oversight considerations to solution designs.
• Ability to critically evaluate AI use cases, including expected value, data dependencies, constraints, operational impacts and circumstances where AI should not be used.
• Experience creating architecture artefacts such as solution designs, integration views, data flows, decision records, standards and reusable patterns.
• Previous experience in an organisation with established AI architecture governance, including design assurance, standards, guardrails and architectural decision-making.
• Experience identifying common needs across projects and translating them into reusable components, shared services or enterprise patterns.
• Ability to maintain a portfolio-level view of AI initiatives, manage architectural dependencies and challenge unnecessary duplication.
• Stakeholder-management skills and confidence engaging business leaders, architects, engineers, risk specialists and delivery teams.
• Ability to facilitate workshops, elicit requirements, constructively challenge assumptions and build agreement across groups with different priorities.
• Clear written and verbal communication, including the ability to explain complex AI concepts in straightforward language to non-technical audiences.
• Collaborative and pragmatic approach, balancing strategic direction, governance and delivery needs across multiple initiatives.
• Curiosity and sound judgement, with a commitment to continuous learning in a changing technology and regulatory environment.

Nice-to-haves

• Experience in insurance, reinsurance or another regulated financial-services environment.
• Knowledge of Microsoft AI services and tools such as Azure AI Foundry, Azure OpenAI, Copilot Studio or Microsoft Fabric.
• Experience with Databricks, data platforms, semantic technologies, knowledge graphs or enterprise data governance.
• Familiarity with AI risk-management frameworks, model governance, threat modelling and emerging AI regulation.
• Experience supporting proofs of concept through production adoption, including defining success measures and evaluating solution quality.
• Relevant cloud, architecture, data, security or AI certifications.

Benefits

• Medical plans for employees and their families
• Health and wellness programmes
• Retirement plans
• Tuition reimbursement
• Paid annual leave or vacation
• Competitive target incentive compensation based on corporate and individual performance
• Hybrid work model
• Career growth and professional development opportunities