Platform Owner (AI Platform) at RAKBANK

Dubai, Dubai, United Arab Emirates -

Full Time

Start Date

Immediate

Expiry Date

08 Apr, 26

Salary

0.0

Posted On

08 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Platform Ownership, LLM Management, Cloud Deployment, Data Compliance, Cost Management, AI Use Case Enablement, Platform Engineering, Vendor Management, Team Leadership, Stakeholder Engagement, Model Risk Management, AI Tooling, APIs, MLOps, Prompt Engineering, Data Privacy, Regulatory Compliance

Industry

Banking

Description

Company Description 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: Platform Owner: AI 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

Responsibilities

The AI Platform Owner is responsible for owning, building, scaling, and operating the enterprise AI platform for the bank. This includes managing AI tools, ensuring security and scalability, and enabling multiple AI use cases across various business functions.