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.

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

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