AI Technical Governance

AI Technical Governance & Delivery Lead Job Overview We are looking for an experienced AI Technical Governance & Delivery Lead to provide technical leadership, architecture governance, and delivery oversight across multiple enterprise AI initiatives and development squads. The role will be responsible for governing AI technical delivery, reviewing and approving solution architectures, ensuring adherence to enterprise technology standards, and providing technical direction across Generative AI, Machine Learning, AI Agents, RAG, analytics, and AI platforms. The successful candidate will work closely with solution architects, AI engineers, developers, data engineers, cloud and security teams, vendors, and senior stakeholders to ensure AI solutions are scalable, secure, compliant, production-ready, and aligned with enterprise architecture and business objectives.

Key Responsibilities
1. Technical Delivery Governance Govern technical delivery activities across multiple AI development squads. Review solution architectures, technical designs, and implementation approaches. Ensure consistency in architecture, engineering practices, technology standards, and delivery methodologies. Track technical progress, delivery milestones, dependencies, risks, and production readiness. Identify technical risks and drive appropriate mitigation actions. Review and approve solution designs prior to implementation and production deployment. Govern internal and external delivery teams to ensure adherence to approved architectures, technical standards, and roadmaps. 2. Solution Architecture Leadership Define and establish enterprise AI solution architecture standards, principles, and best practices. Review and validate AI architectures covering LLMs, RAG, AI Agents, Machine Learning, analytics, and enterprise AI platforms. Ensure AI solutions meet requirements for scalability, maintainability, performance, reliability, and security. Govern integration approaches with enterprise applications, data platforms, APIs, and technology ecosystems. Provide architectural direction and technical guidance for complex AI initiatives. Challenge and validate architecture decisions to ensure alignment with enterprise technology strategy. 3. Squad Leadership & Technical Coordination Act as the technical lead across multiple AI delivery squads. Provide technical direction to solution architects, developers, AI engineers, ML engineers, and data engineers. Facilitate technical decision-making, architecture reviews, design workshops, and technical assurance sessions. Resolve cross-team technical dependencies, risks, and delivery challenges. Establish engineering quality standards and technical delivery practices. Ensure consistency in design, implementation, integration, testing, and operational readiness across AI initiatives. 4. AI Platform & Environment Oversight Govern the utilization of AI platforms, development environments, and deployment practices. Ensure AI solutions align with cloud, infrastructure, cybersecurity, data, and enterprise architecture standards. Review MLOps/LLMOps, CI/CD, monitoring, observability, model lifecycle management, and operational support capabilities. Ensure AI solutions are production-ready, scalable, supportable, and aligned with enterprise operational requirements. Provide technical oversight of cloud-based AI environments across platforms such as Azure, AWS, OCI, or GCP. 5. Quality, Security & Compliance Ensure AI implementations comply with enterprise technical governance and security controls. Review AI model risks, performance metrics, reliability, and operational safeguards. Ensure implementation of Responsible AI, privacy, security, auditability, and regulatory requirements. Lead technical assurance reviews and deployment readiness assessments. Collaborate with cybersecurity, data governance, risk, and compliance teams to address AI-related risks. 6. Stakeholder & Vendor Management Communicate technical status, architecture decisions, risks, dependencies, and recommendations to senior leadership. Provide technical insights and recommendations within project and technology governance forums. Work closely with business stakeholders, enterprise architects, technology teams, vendors, and delivery partners. Govern technical contributions from external vendors and system integrators. Support strategic technology decisions and AI roadmap planning. Qualifications Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, Information Technology, or a related discipline. Strong understanding of Generative AI, Machine Learning, AI Agents, RAG architecture, cloud platforms, and enterprise application architecture. Relevant certifications in Enterprise Architecture, Cloud, Artificial Intelligence, Azure, AWS, OCI, GCP, or equivalent technologies are preferred. Knowledge of Responsible AI, AI governance, MLOps/LLMOps, cybersecurity, and enterprise architecture frameworks