AI Technical Governance

Job Description

REQUIREMENTS

• 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.

Required Experience

• 8–12 years of experience in solution architecture, technical delivery management, AI solutions, or enterprise technology environments.
• Proven experience leading technical delivery across multiple teams, projects, or AI development squads.
• Strong experience designing, reviewing, and governing enterprise solution architectures.
• Hands-on or strong practical experience with Generative AI, LLMs, RAG, AI Agents, Machine Learning, and enterprise AI platforms.
• Experience reviewing solution designs and ensuring adherence to architecture, security, engineering, and delivery standards.
• Experience with cloud platforms such as Azure, OCI, AWS, or GCP.
• Experience managing technical dependencies, risks, issues, and delivery challenges across multiple teams and vendors.
• Experience working within government, public sector, financial services, healthcare, telecom, or other regulated environments is highly desirable.
• Experience governing enterprise AI implementations and technical delivery programs.
• Strong experience collaborating with architects, engineering teams, cybersecurity, infrastructure, data, and business stakeholders.

RESPONSIBILITES

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.

Are you interested in this position?

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