Develop and continuously refine a multi-year (e.g. 3-year) AI strategy and roadmap for the COO division, aligned to the bank's Winning Together 2029 (WT29) strategy and broader group priorities.
Identify, evaluate, and prioritize AI use cases and opportunities across COO functions based on business value, feasibility and organizational readiness.
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Technical Leadership & Solution Ownership: Lead the technical direction of AI initiatives by driving architecture decisions, establishing best practices, mentoring team members, and guiding the end-to-end delivery of scalable AI solutions.
Collaboration & Delivery: Work with Product Owner, Developers, Quality Engineers and UX Designers to deliver the Virtual Companion. Contribute to sprint planning, code reviews and documentation.
Bachelor’s degree or above, with 5-8 years in software development and relevant experience in leading AI/ML development project
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Assess the commercial potential and technical readiness levels of internal R&D projects for potential spin-offs or new business incubation.
Bachelor's degree in Engineering (e.g., Electrical, Mechanical, Chemical, Software, Materials Science, Biomedical) or a relevant scientific discipline from an accredited institution.
Master's or Ph.D. in a relevant engineering or scientific field is highly preferred.
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Map how each department works today and rank tasks by time saved, cost and risk.
Keep a live AI roadmap and use-case register for the whole group.
Build and maintain AI agents and automations: quotation and RFQ drafting, contract review, tender and lead research, reporting, HR and payroll admin, and site-worker communication.
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Operational Governance: Maintaining operational governance of AI solutions by managing production stability, performance thresholds, and compliance requirements.
Stakeholder Collaboration: Collaborating with application operations, platform operations, engineering, architecture, and business stakeholders to design, deploy, and improve agent-based solutions. Compute Fundamentals: Demonstrates a deep understanding of compute concepts, including virtualization, containerization, operating systems, and system administration. Cloud Technologies: Experience with cloud platforms like GCP, AWS, and Azure, including their AI/ML services. Generative AI Experience with LLM and Generative AI and Google Cloud Products and services (e.g Vertex AI, Dialogflow, Gemini) ML Development: Experience with Machine Learning model development and deployment. AIML Frameworks: Experience with frameworks for deep learning (e.g. PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g. TPUs, GPUs), model architectures (e.g. encoders, decoders, transformers), and using machine learning APIs. Must have Associate Cloud Engineer (ACE) Certification Malaysia Software Engineering Professional PETALING JAYA, MY (0088) IBM Malaysia Sdn. Bhd.
Operational Governance: Maintaining operational governance of AI solutions by managing production stability, performance thresholds, and compliance requirements.
Stakeholder Collaboration: Collaborating with application operations, platform operations, engineering, architecture, and business stakeholders to design, deploy, and improve agent-based solutions.
Compute Fundamentals: Demonstrates a deep understanding of compute concepts, including virtualization, containerization, operating systems, and system administration.
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