AI Technical Governance

Gibraltar Technologies LLC; Dubai; On-site; AED 180,000 - 300,000; Full time

Job summary

Gibraltar Technologies LLC is seeking an AI Technical Governance & Delivery Lead to provide technical leadership, architecture governance, and delivery oversight across multiple enterprise AI initiatives. You will govern AI technical delivery, review architectures, and guide solutions across LLMs, RAG, AI Agents, ML, analytics, and AI platforms.

Collaborate with solution architects, AI engineers, data engineers, and security teams to ensure scalable, secure, production-ready AI solutions aligned

Qualifications

• Bachelor's or Master's degree in CS/AI/SE/IT.
• Strong understanding of Generative AI, ML, AI Agents, RAG architecture, cloud platforms, and enterprise app architecture.
• Certifications in Enterprise Architecture, Cloud, AI (Azure/AWS/OCI/GCP) preferred.
• Knowledge of Responsible AI, AI governance, MLOps/LLMOps, cybersecurity, and enterprise architecture frameworks.

Responsibilities

• Govern technical delivery across multiple AI squads.
• Review solution architectures, designs, and implementation approaches.
• Ensure consistency in architecture, standards, and delivery methodologies.
• Track progress, milestones, dependencies, risks, and production readiness.
• Identify technical risks and drive mitigations.
• Review and approve designs prior to production deployment.
• Govern delivery teams to ensure adherence to architectures and roadmaps.
• Define enterprise AI solution architecture standards and best practices.
• Review AI architectures: LLMs, RAG, AI agents, ML, analytics, and platforms.
• Ensure scalability, maintainability, performance, reliability, and security.
• Govern integration with enterprise apps, data platforms, APIs, and ecosystems.
• Provide architectural direction for complex AI initiatives.
• Challenge decisions to align with technology strategy.
• Act as technical lead across multiple AI squads.
• Provide direction to solution architects, developers, AI/ML engineers, data engineers.
• Facilitate architecture reviews, design workshops, and assurance sessions.
• Resolve cross-team dependencies, risks, and delivery challenges.
• Establish engineering quality standards and delivery practices.
• Ensure consistent design, integration, testing, and operations across AI initiatives.
• Govern AI platform usage, environments, and deployment practices.
• Align AI with cloud, security, data, and enterprise standards.
• Review MLOps/LLMOps, CI/CD, monitoring, lifecycle management.

Skills

Generative AI

AI Agents

RAG architecture

Machine Learning

Enterprise architecture

Education

Computer Science / AI / IT degree

Tools

Azure

AWS

GCP

OCI

Job description

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.

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.

Desired Candidate Profile

AI Technical Governance | Enterprise AI Architecture | Technical Delivery Leadership | GenAI | LLM | RAG | AI Agents | MLOps/LLMOps | Cloud | Enterprise Integration | AI Security & Responsible AI