Director, AI Platform & Governance

Director, AI Platform & Governance Position Responsibilities: 1. AI Platform Strategy & Roadmap • Own the enterprise AI Platform strategy, roadmap, and target-state architecture. • Define and evolve the organization’s AI capabilities, standards, and operating model. • Establish platform capabilities that enable rapid, secure, and reusable AI solution development across business domains. • Partner with business, Technology, and Data leaders to understand current and future AI requirements. • Develop a multi-year roadmap covering AI infrastructure, platform capabilities, governance controls, developer experience, and operational excellence. • Evaluate emerging AI technologies and define adoption strategies aligned with enterprise objectives. 2. AI Platform Ownership • Own the design, delivery, operation, and continuous enhancement of the enterprise AI Platform. • Lead implementation of core AI Platform capabilities, including: • AI Gateway • LLM Platform • Prompt Management • Agentic AI Framework • Knowledge Retrieval and RAG Services • Model Registry • Vector Databases • AI Workflow Orchestration • Model Monitoring and Observability • Cost and Usage Management • AI Development Tooling • Establish reusable platform services that minimize duplication across use cases and business domains. • Drive platform reliability, scalability, resilience, and performance. • Ensure AI platform services meet enterprise availability and operational requirements. 3. AI Governance & Responsible AI • Establish and maintain enterprise AI governance frameworks aligned with regulatory requirements and internal policies. • Lead implementation of governance controls throughout the AI lifecycle. • Ensure AI systems are developed, deployed, monitored, and retired in accordance with enterprise standards. • Define model registration, approval, monitoring, validation, and review processes. • Establish responsible AI controls covering fairness, transparency, explainability, accountability, and human oversight. • Partner with Risk, Compliance, Legal, and Internal Audit to ensure AI governance requirements are effectively embedded within platform processes. • Support regulatory examinations, audits, model reviews, and governance reporting. 4. MLOps, LLMOps & AI Operations • Establish enterprise MLOps and LLMOps capabilities. • Define standards and tooling for model deployment, testing, versioning, monitoring, rollback, and lifecycle management. • Implement automated pipelines supporting AI development and deployment. • Establish model performance monitoring and drift detection frameworks. • Define operational processes for incident management, service recovery, and platform support. • Drive observability across AI systems, models, prompts, agents, and infrastructure components. • Ensure platform utilization, cost efficiency, and resource optimization. 5. Security, Risk & Compliance • Partner with Information Security and Enterprise Architecture teams to establish secure AI architectures. • Implement controls for access management, data protection, secret management, model access, and API security. • Ensure compliance with enterprise security standards and regulatory obligations. • Establish risk assessment and approval processes for AI solutions. • Define technical guardrails to prevent misuse, data leakage, unsafe outputs, and policy violations. • Support threat modelling and security reviews for AI systems and infrastructure. 6. Technical Architecture & Engineering Leadership • Provide technical leadership and architectural direction across AI technologies. • Establish standards for: • Generative AI • AI Agents • Machine Learning Platforms • Prompt Engineering • RAG Architectures • Knowledge Management • AI APIs and Integrations • Cloud-native AI Services • Review solution architectures and provide guidance to delivery squads. • Promote engineering best practices, reusable patterns, and platform-first thinking. • Partner with Enterprise Architecture to ensure alignment with broader technology strategy. 7. Squad Leadership • Lead the AI Platform & Governance Squad comprising AI engineers, platform engineers, MLOps engineers, and governance specialists. • Establish squad objectives, priorities, delivery plans, and performance measures. • Drive execution against platform and governance roadmaps. • Foster close collaboration with business domain squads, Data teams, Technology teams, Risk, Compliance, and Security stakeholders. • Promote a culture of engineering excellence, reliability, automation, continuous learning, and operational discipline. • Develop technical capabilities within the squad and create a high-performing engineering environment. • Build strong engineering practices across AI development, deployment, monitoring, and governance. Required Qualifications: • Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, or a related discipline. • Master's degree preferred. • 10+ years of experience in AI, machine learning, software engineering, platform engineering, cloud engineering, or related fields. • 5+ years of experience leading technical teams, platforms, or engineering organizations. • Proven experience building and operating enterprise-scale AI, machine learning, or cloud platforms. • Experience leading enterprise architecture, platform engineering, MLOps, LLMOps, or AI infrastructure initiatives. • Experience implementing AI governance and model risk management processes within regulated industries. • Financial services or insurance experience strongly preferred. • Deep understanding of Generative AI, large language models, AI agents, and modern AI architectures. • Strong knowledge of AI Platform technologies and patterns, including: • MLOps • LLMOps • RAG • Vector Databases • AI Gateways • AI Agent Frameworks • Model Registries • Observability Platforms • Strong cloud architecture knowledge across Azure, AWS, Alibaba Clo