Director, AI Platform & Governance

Manulife John Hancock; Hong Kong; On-site; HKD 1,800,000 - 2,400,000; Full time

Job summary

Manulife John Hancock in Hong Kong seeks a Director to lead the AI Platform & Governance function. You will shape enterprise AI strategy, governance, and MLOps, partnering with business and technology leaders to deliver scalable, secure AI capabilities.

You will drive platform architecture, define standards for governance, risk, and responsible AI, and lead a cross-functional squad of AI engineers, platform engineers, and governance specialists.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, AI, or related field.
• Master's degree preferred.
• 10+ years of experience in AI, machine learning, software engineering, platform engineering, cloud engineering.

Responsibilities

• Own the enterprise AI Platform strategy, roadmap, and target-state architecture.
• Define and evolve AI capabilities, standards, and operating model.
• Establish platform capabilities enabling rapid, secure AI development across domains.

Skills

AI Platform Leadership

Governance

MLOps

Security

Compliance

Architecture

Cloud Engineering

Education

Bachelor's degree in Computer Science/Software Eng/Data Science

Master's degree preferred

Tools

AI Gateway

LLM Platform

Prompt Management

Agentic AI Framework

Knowledge Retrieval & RAG

Model Registry

Vector Databases

AI Workflow Orchestration

Model Monitoring

Cost & Usage Mgmt

AI Development Tooling

Job description

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 engineerin