Platform Owner

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 ObjectivesEstablish 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 toolingKey Responsibilities 1. AI Platform Ownership & StrategyOwn 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 ManagementOwn 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 reliabilityEnable 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 infrastructureEnsure:High availability, resilience, and scalability Secure data handling and isolation Compliance with data residency and regulatory requirementsIntegrate AI platform with:Core banking and enterprise systems APIs, event platforms, data platforms, and digital channels4. AI Use Case EnablementWork 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 componentsEnable 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 ManagementOwn run operations of the AI platform:Monitoring, performance, incidents, and SLAs Model drift, accuracy, and quality controlsOwn change delivery:Platform enhancements New model onboarding Capability upgradesEstablish clear ownership, escalation, and support models6. Cost Management & OptimizationManage AI platform costs across:Model usage (tokens, inference) Compute, storage, and networking Tooling and licensingDefine and enforce:Cost visibility and chargeback/showback models Usage limits and optimization strategiesBalance innovation speed vs cost efficiency7. Governance, Risk & ComplianceEmbed Responsible AI principles into the platform Ensure compliance with:Model risk management Data privacy and security Regulatory and audit requirementsImplement:Model explainability and auditability Logging, traceability, and controlsPartner closely with Risk, Compliance, Legal, and Security teams8. Vendor & Ecosystem ManagementManage relationships with:Cloud providers AI vendors and open‑source communities System integrators and partnersStay current with rapid AI market evolution and assess impact to the bank Continuously evaluate emerging technologies and platforms9. Leadership & CollaborationLead and grow a high‑performing AI platform engineering team Work in close partnership with:Enterprise Architecture Platform Owners Data, Digital, Security, and Engineering teamsAct as the AI technology evangelist within the organizationLeadership & DeliveryProven 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 scaleBusiness & Stakeholder SkillsStrong ability to translate business needs into AI platform capabilities Comfortable engaging with senior stakeholders across business and technology Strong communication and decision‑making skillsPreferred ExperienceExperience 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 platformsQualifications Minimum Requirements Technical Expertise10+ 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 architecturesDeep understanding of:LLM architectures, prompt engineering, embeddings, vector databases AI model lifecycle management and MLOps/LLMOps#J-18808-Ljbffr