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…