Responsibilities

Manage portal development roadmap and prioritise timeline with release development team for Enterprise products.
Enhance portal UI/UX through design thinking methodologies and benchmarking against global standards, best practices, and API trends.
Develop initiatives to enhance service orchestration and automation to support multi-vendor, multi-technology, multi-services solutioning and eco-systems.
Own the product roadmap, feature prioritisation and lifecycle management of the AI Studio platform, ensuring alignment with business, customer and digital transformation objectives.
Drive the development of AI Studio capabilities, including GenAI applications, AI agents, prompt management, agentic workflow automation, knowledge integration and self-service AI solutions.
Partner with business, engineering, cybersecurity, data and governance teams to identify, evaluate and onboard AI use cases that drive productivity, operational efficiency and customer experience improvements.
Define and implement AI governance practices, including responsible AI, security, compliance, privacy and model risk management requirements.
Evaluate and integrate AI technologies, LLMs, AI APIs and ecosystem partners to enhance AI Studio capabilities and user experience.
Establish usage metrics, adoption KPIs and value-realisation frameworks to measure AI Studio business outcomes and continuous improvement opportunities.
Support initiatives that empower AI to deliver advanced analytics, predictive insights and automation for portal users.
Support monetisation strategies to provide XaaS solutions for value generation.
Align portal and AI Studio development user stories, product backlogs and sprint goals from key stakeholders.

Requirements

Min Bachelor Degree in Computing or Business management
More than 3 years’ experience in fields relating to AI/GenAI product management, AI platforms or digital products.
Good understanding of Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), prompt engineering and AI orchestration frameworks.
Understanding of Model Context Protocol (MCP) and agentic AI architectures, including integration of AI assistants and agents with enterprise systems, APIs, tools, knowledge bases and workflows to enable secure, scalable and interoperable AI solutions.
Knowledge of AI governance, responsible AI, cybersecurity, compliance and data privacy requirements.
Experience with API-first architectures, SDKs, AI model integration and ecosystem partnerships.
Ability to translate business requirements into scalable AI-enabled solutions with measurable business outcomes.