Director, AI Engineering

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Job Description:

The Director of AI Engineering, Corporate IT leads the engineering of artificial intelligence for internal enterprise delivery, from strategy and architecture through production delivery. This role builds secure, scalable and responsible AI capabilities that modernize enterprise systems, automate IT operations and improve employee productivity.

What will you do?

AI Strategy & Roadmap

· Own the Corporate IT AI strategy and roadmap, aligned with CIO and enterprise priorities.

· Lead intake, value assessment and prioritization of AI use cases across functions such as Actuarial, Risk, Compliance, Human Resources, Finance, Legal, Procurement etc.

· Manage AI licensing, vendor contracts and cloud spend within the IT budget.

· Rationalize AI tools, reduce duplication and address unapproved or shadow AI use.

· Assess emerging capabilities, including copilots, agents and SaaS AI features, for enterprise fit.

Enterprise AI Patterns, Architecture & Integration

· Define AI patterns and align them with Enterprise Architecture standards.

· Guide integration of AI with collaboration platforms and enterprise data services.

· Govern build-versus-buy decisions across packaged AI, SaaS capabilities and custom solutions.

· Align AI solutions with identity and access management, single sign-on, API gateways and enterprise data platforms.

· Act as the AI design authority in relevant architecture review forums.

AI Platform, AIOps & Automation

· Build and operate enterprise AI platform capabilities, including LLM gateways, retrieval-augmented generation, vector stores, prompt management and evaluation.

· Deliver AIOps capabilities such as event correlation, anomaly detection and automated remediation.

· Apply AI to IT service management, including ticket triage, routing, virtual agents and knowledge search.

· Implement MLOps and LLMOps pipelines integrated with IT change and release management.

· Monitor model quality, drift, hallucination risk, service performance and inference cost.

Security, Risk & Responsible AI

· Embed information security, data classification and data loss prevention requirements across AI services.

· Protect AI solutions against data leakage, prompt injection and model abuse.

· Partner with Risk, Legal, Compliance and Privacy to establish and operate AI controls.

· Support privacy, SOX, model-risk and audit requirements for AI-enabled corporate processes.

· Maintain an accurate inventory of AI solutions and an approved-tools catalogue.

People, Talent & Adoption

· Lead AI engineering, platform and automation teams and establish clear accountabilities.

· Design roles, career paths and team structures that build sustainable AI capability within Corporate IT.

· Mentor IT employees in AI engineering and secure, responsible AI practices.

· Drive employee AI adoption and literacy in partnership with corporate function owners.

· Manage strategic partners and managed service providers and hold them accountable for outcomes.

IT Service Delivery & Operations

· Deliver AI initiatives through established portfolio, change, release and governance processes.

· Own service-level expectations, support models, runbooks and operational readiness for AI services.

· Partner with corporate function owners to define outcomes and realize measurable value.

· Report value, risk, delivery and spend to the CIO and IT leadership.

· Lead change management and communications for AI rollouts.

What do you need to succeed?

· Demonstrated hands-on experience building and delivering artificial intelligence or machine learning solutions into production, supported by a strong foundation in software engineering, data engineering or machine learning.

· Experience designing AI engineering patterns and guiding engineering teams through technical implementation while remaining actively involved in solution development.

· Proficiency with Python and experience with data integration, data pipelines or extract, transform and load processes.

· Experience with cloud AI services and application programming interfaces on Amazon Web Services, Microsoft Azure or both.

· Practical knowledge of generative AI, large language models, retrieval-augmented generation, model evaluation and production monitoring.

· Experience navigating information security, data protection, responsible AI and technology-governance requirements in a complex enterprise environment.

· Ability to explain complex technical recommendations to engineering teams, senior leaders and business stakeholders, mentor colleagues and build alignment across teams.

· Experience working in a regulated or highly governed enterprise environment.

· Amazon Web Services, Microsoft Azure or AI-related training or certification.

· Degree or advanced education in computer science, software engineering, data science, machine learning or a related discipline, or equivalent work experience…