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 Cloud, or equivalent environments.

Experience with containerization, Kubernetes, API management, and modern DevSecOps practices.

Experience designing scalable, secure, and highly available enterprise platforms.

Strong understanding of AI security, privacy, and governance requirements.

Hands-on experience with Python and modern AI engineering frameworks.

Strategic technology leadership.

Strong architecture and systems thinking.

Excellent stakeholder management and influencing skills.

Ability to communicate complex technical concepts to executive audiences.

Strong execution focus with a demonstrated ability to deliver enterprise platforms at scale.

Ability to balance technology advancement, governance, security, and operational excellence.

Strong problem-solving and decision-making capability.

Passion for building enterprise AI capabilities that enable business transformation.

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Working Arrangement

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