Platform Owner-AI Platform

At RAKBANK , we believe in fostering a culture of innovation, growth, and excellence. We are not just a bank – we are a community that thrives on teamwork, cutting-edge solutions, and the highest standards of governance.

Job Description

Role Purpose:

The AI Platform Owner is responsible for owning, building, scaling, and operating the enterprise AI platform for the bank. This role leads the technology side of AI enablement , ensuring that Large Language Models (LLMs), AI tools, and agentic capabilities are secure, scalable, cost-effective, and reusable across business and technology use cases.

The role acts as the single point of accountability for the AI platform across cloud and on-prem environments , balancing rapid innovation with operational stability, governance, and regulatory compliance .

Key Objectives

• Establish a bank-wide AI platform that enables fast, safe, and scalable AI adoption
• Enable multiple AI use cases across business, operations, risk, compliance, and technology
• Manage AI run & change with strong cost, performance, and reliability controls
• Continuously evolve the platform in line with fast-paced advancements in LLMs and AI tooling

Key Responsibilities

1. AI Platform Ownership & Strategy

• Own the end-to-end AI platform roadmap , architecture, and operating model
• Define the AI platform vision aligned to enterprise technology and business strategy
• Decide build vs buy vs partner for AI tooling, models, and platforms
• Ensure the platform supports current and future AI paradigms (GenAI, Agentic AI, AI-augmented development, AI-Ops)

2. LLM & AI Technology Management

• Own lifecycle management of LLMs and SLMs (open-source and commercial)
• Manage:
• Model selection and evaluation
• Fine-tuning, prompt strategies, embeddings, and vector stores
• Inference optimization, latency, throughput, and reliability
• Enable multi-model and multi-provider strategy (cloud, on-prem, hybrid)

3. Platform Engineering (Cloud & On-Prem)

• Lead deployment and operations of AI platforms across:
• Public cloud
• Private cloud / on-prem infrastructure
• Ensure:
• High availability, resilience, and scalability
• Secure data handling and isolation
• Compliance with data residency and regulatory requirements
• Integrate AI platform with:
• Core banking and enterprise systems
• APIs, event platforms, data platforms, and digital channels

4. AI Use Case Enablement

• Work closely with business, operations, risk, compliance, and technology teams to:
• Identify high-value AI use cases
• Prioritize and onboard use cases onto the platform
• Provide reusable AI services, APIs, and components
• Enable rapid experimentation while maintaining enterprise controls
• Support AI use cases across:
• Customer experience
• Operations & automation
• Risk, compliance & fraud
• Engineering productivity (AI-augmented development)

5. Run & Change Management

• Own run operations of the AI platform:
• Monitoring, performance, incidents, and SLAs
• Model drift, accuracy, and quality controls
• Own change delivery :
• Platform enhancements
• New model onboarding
• Capability upgrades
• Establish clear ownership, escalation, and support models

6. Cost Management & Optimization

• Manage AI platform costs across:
• Model usage (tokens, inference)
• Compute, storage, and networking
• Tooling and licensing
• Define and enforce:
• Cost visibility and chargeback/showback models
• Usage limits and optimization strategies
• Balance innovation speed vs cost efficiency

7. Governance, Risk & Compliance

• Embed Responsible AI principles into the platform
• Ensure compliance with:
• Model risk management
• Data privacy and security
• Regulatory and audit requirements
• Implement:
• Model explainability and auditability
• Logging, traceability, and controls
• Partner closely with Risk, Compliance, Legal, and Security teams

8. Vendor & Ecosystem Management

• Manage relationships with:
• Cloud providers
• AI vendors and open-source communities
• System integrators and partners
• Stay current with rapid AI market evolution and assess impact to the bank
• Continuously evaluate emerging technologies and platforms

9. Leadership & Collaboration

• Lead and grow a high-performing AI platform engineering team
• Work in close partnership with:
• Enterprise Architecture
• Platform Owners
• Data, Digital, Security, and Engineering teams
• Act as the AI technology evangelist within the organization

Leadership & Delivery

• Proven experience owning enterprise-scale platforms
• Strong run + change ownership mindset
• Ability to operate in fast-changing, ambiguous environments
• Experience managing cost, performance, and reliability at scale

Business & Stakeholder Skills

• Strong ability to translate business needs into AI platform capabilities
• Comfortable engaging with senior stakeholders across business and technology
• Strong communication and decision-making skills

Preferred Experience

• Experience in banking or regulated industries
• Exposure to AI governance, model risk, and regulatory frameworks
• Experience with agentic AI or autonomous workflows
• Background in developer platforms or internal product platforms

Qualifications

Minimum Requirements:

Technical Expertise

• 10+ years in platform, cloud, or enterprise engineering roles
• Strong hands-on experience with:
• LLM-based platforms and AI tooling
• Cloud and/or on-prem AI deployments
• APIs, microservices, data platforms, and event-driven architectures
• Deep understanding of:
• LLM architectures, prompt engineering, embeddings, vector databases
• AI model lifecycle management and MLOps/LLMOps

Email detailed Cv to absai.gamariel@rakbank.ae