Commercial Bank of Dubai
AI Platforms and Adoption Lead
Commercial Bank of DubaiUnited Arab Emirates12 hours ago
Full-timeOther
Job Purpose:

Operating in the UAE for over 50 years, CBD manages the financial requirements of some of the largest corporates and businesses operating in the country, driving the UAE economy. Over the years, CBD has transformed into a progressive and modern banking institution, winning multiple awards for its digital initiatives, credit cards, bank accounts, mobile app features, and services. CBD has been recognized as the number one bank in the UAE on the Forbes list of The World’s Best Banks 2022.

CBD is embarking on a transformative journey to modernize its use of data and operationalize its data strategy and operating model. The data strategy is underpinned by design principles of revenue generation, increasing operational efficiency, and improving financial and regulatory controls. Effective and well-governed data will also aid in improving the outcomes of various analytics and AI use cases that the bank aims to implement.

We are seeking an experienced Leader in Artificial Intelligence to drive critical elements of the execution of the bank’s AI strategy. The purpose of the AI Platforms & Adoption Lead is to work within the AI team to deliver the bank’s AI ambition on an end-to-end responsibility basis. This includes analysing and researching AI and machine learning solutions. The role holder will need to conduct assessments on AI ROI, feasibility on AI projects, and drive the required AI governance for a large scale AI transformation program. The purpose of the role includes overseeing the AI agent and to advise the bank’s C-Suite on the latest trends and issues on AI.

Principal Accountabilities

  • Own the end‑to‑end execution of the bank’s AI strategy, translating strategic ambition into an executable roadmap that delivers 250 production‑grade AI agents within 18 months.
  • Define and manage a prioritised AI agent portfolio, aligned to business value, risk appetite, and regulatory constraints.
  • Establish clear success metrics (e.g. productivity uplift, cost take‑out, risk reduction, customer experience impact) and ensure benefits realisation.
  • Act as the single accountable owner for AI platform delivery, adoption, and scale across the bank.
  • Lead the design, build, and evolution of the bank’s AI agent platforms, including: LLM orchestration and agent frameworks, Data pipelines and feature stores, Model lifecycle management (MLOps / LLMOps), and Secure integration with core banking and enterprise systems
  • Ensure platforms are secure, scalable, resilient, and cloud‑ready, capable of supporting hundreds of concurrent AI agents.
  • Drive standardisation and reuse, reducing time‑to‑deploy per agent while maintaining quality and control.
  • Partner with Architecture, Cybersecurity, and Infrastructure teams to ensure alignment with enterprise standards.
  • Establish and run an “AI Agent Factory” operating model to industrialise agent development and deployment.
  • Define repeatable patterns, templates, and toolkits to enable rapid agent creation across business units.
  • Oversee the full AI agent lifecycle: Use‑case intake and qualification, Design and development
  • Testing, validation, and risk sign‑off, and Production deployment and monitoring
  • Continuously optimise delivery velocity to meet aggressive scale targets without compromising control or quality.
  • Drive enterprise‑wide adoption of AI agents, ensuring they are embedded into real workflows, not run as pilots.
  • Partner with business leaders to: Identify high‑impact use cases, Redesign processes around AI agents, Measure and realise tangible outcomes, Lead change management, training, and enablement programmes to build confidence and capability across the bank.
  • Act as a trusted advisor to senior stakeholders on how AI can transform productivity and decision‑making.
  • Embed Responsible AI, model risk management, and regulatory compliance into all AI platforms and agents by design.
  • Work closely with Risk, Compliance, Legal, and Model Validation teams to: Define AI controls, policies, and guardrails, Support internal and external audits, and Ensure explainability, traceability, and monitoring of AI agents
  • Ensure AI agents meet the bank’s standards for data privacy, security, fairness, and operational resilience.
  • Evaluate, select, and manage AI technology vendors, hyperscalers, and fintech partners.
  • Negotiate commercial models that support rapid scale while controlling cost and vendor lock‑in.
  • Ensure external solutions integrate cleanly into the bank’s AI platforms and governance frameworks.
  • Stay ahead of market developments in agentic AI, foundation models, and tooling.
  • Define and implement the target operating model for AI platform delivery and adoption.
  • Build, lead, and upskill cross‑functional teams spanning engineering, data science, product, and change.
  • Introduce agile and product‑centric ways of working suited to AI delivery at scale.
  • Mentor internal teams and help transition from dependency on external vendors to strong internal AI capability.
  • Own delivery tracking against the 250‑agent roadmap, including milestones, risks, and dependencies.
  • Proactively surface delivery, risk, or adoption issues and drive corrective action.
  • Demonstrate return on investment and strategic impact of AI platforms and agents.

Requirements:

Qualifications

  • 7 + years of experience in applying AI to practical and comprehensive technology solutions including at least 7 years in positions of progressively increasing managerial responsibilities.
  • Master's degree in computer science, data science, or a related field.
  • 10+ years of banking or financial services experience.
  • Led at least 2 large data transformation programs end-to-end preferably in large multi-national banks.
  • Experience with Copilot, Copilot Studio, Copilot Foundry, Machine Learning, and Natural Language Processing.
  • Experience in program leadership, governance, and change enablement.
  • Knowledge of basic algorithms, object-oriented and functional design principles, and best-practice patterns.
  • Excellent written and verbal communication skills.
  • Analytical and review skills with the ability to draw the correct conclusions from reviews.

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