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Manulife Financial

Global financial services: insurance and investments

Director – AI Platform and Governance

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeHong KongNo salary listed

About the Job

Requirements

  • A bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, Artificial Intelligence, or a related discipline is required.
  • At least 10 years of experience in AI, machine learning, software engineering, platform engineering, cloud engineering, or related fields is required.
  • At least 5 years of experience leading technical teams, platforms, or engineering organizations is required.
  • Proven experience building and operating enterprise-scale AI, machine learning, or cloud platforms is required.
  • Experience leading enterprise architecture, platform engineering, MLOps, LLMOps, or AI infrastructure initiatives is required.
  • Experience implementing AI governance and model risk management processes within regulated industries is required.
  • A deep understanding of Generative AI, large language models, AI agents, and modern AI architectures is required.
  • Strong knowledge of AI platform technologies and patterns, including MLOps, LLMOps, retrieval-augmented generation, vector databases, AI gateways, AI agent frameworks, model registries, and observability platforms, is required.
  • Strong cloud architecture knowledge across Azure, AWS, Alibaba Cloud, or equivalent environments is required.
  • Experience with containerization, Kubernetes, API management, and modern DevSecOps practices is required.
  • Experience designing scalable, secure, and highly available enterprise platforms is required.
  • A strong understanding of AI security, privacy, and governance requirements is required.
  • Hands-on experience with Python and modern AI engineering frameworks is required.
  • Strong architecture and systems thinking, strategic technology leadership, stakeholder management and influencing skills, executive communication, execution focus, problem-solving, and decision-making capabilities are required.
  • The ability to balance technology advancement, governance, security, and operational excellence is required.

Responsibilities

  • 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.
  • Own the design, delivery, operation, and continuous enhancement of the enterprise AI platform.
  • Lead implementation of core AI platform capabilities, including an 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, and AI development tooling.
  • Establish reusable platform services that minimize duplication across use cases and business domains.
  • Drive platform reliability, scalability, resilience, and performance, and ensure services meet enterprise availability and operational requirements.
  • Establish and maintain enterprise AI governance frameworks aligned with regulatory requirements and internal policies.
  • Lead implementation of governance controls throughout the AI lifecycle and 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 embed AI governance requirements within platform processes.
  • Support regulatory examinations, audits, model reviews, and governance reporting.
  • Establish enterprise MLOps and LLMOps capabilities and 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.
  • 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, and 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.
  • 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, and 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.
  • 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, and drive execution against platform and governance roadmaps.
  • Foster collaboration with business domain squads, Data teams, Technology teams, Risk, Compliance, and Security stakeholders.
  • Promote engineering excellence, reliability, automation, continuous learning, and operational discipline.
  • Develop technical capabilities within the squad, create a high-performing engineering environment, and build strong engineering practices across AI development, deployment, monitoring, and governance.

Desired Qualifications

  • A master's degree is preferred.
  • Financial services or insurance experience is strongly preferred.

About the Company

Manulife Financial

Global financial services: insurance and investments

Manulife Financial is a global financial services provider offering a wide range of insurance and investment solutions. It serves individuals, businesses, and institutions with products such as life and health insurance, wealth management, and retirement plans. Customers fund these products through premiums, fees for asset management, and investment income earned on invested assets. Manulife uses analytics and technology to tailor services and improve efficiency, aiming to help clients protect their finances, grow their wealth, and plan for retirement. The company differentiates itself through its large international presence in North America, Asia, and Europe, its breadth of products, and its emphasis on data-driven insights to personalize offerings. Its goal is to help customers achieve their financial goals and aspirations by providing comprehensive, paid-for financial solutions and ongoing support across multiple regions.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1887

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