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About the Role We are seeking a Head of AI Productivity & Governance / AI Solution & Governance Architect — a hands-on AI architecture and solution leader responsible for establishing the company's enterprise AI productivity and acceptance framework. Reporting directly to the CEO, you will work across business and technology teams to ensure AI initiatives deliver measurable business value, operational efficiency, and enterprise-wide adoption. Rather than focusing solely on AI model development, this role involves designing and implementing the AI productivity acceptance measurement framework, also defines how AI-generated work should be evaluated, governed, and continuously improved before becoming part of the company's daily operations. You will establish scalable AI operating standards, productivity measurement frameworks, and governance practices that enable AI to become a trusted capability across investment, trading, operations, finance, and corporate functions. Responsibilities Partner directly with the CEO: to define and execute the company's AI productivity and transformation roadmap. Establish enterprise standards: for evaluating AI-generated outputs, workflows, and business outcomes. Design scalable AI acceptance and governance frameworks: to ensure AI solutions are reliable, measurable, and production-ready. Define productivity KPIs, quality standards, and business acceptance criteria: for AI initiatives across the organization. AI Tooling: Develop and maintain an automated AI acceptance platform that integrates stress testing, chaos engineering (fault injection), A/B testing traffic splitting, and model drift detection, achieving automated and standardized acceptance processes to reduce the average acceptance cycle by over 30%. Establish governance mechanisms: covering AI quality, traceability, risk management, compliance, and continuous improvement. Evaluate AI technologies: and enterprise AI platforms, providing recommendations based on both technical capability and business impact. Develop executive dashboards: and reporting frameworks to measure AI adoption, productivity improvement, business value, and return on investment. Governance and Acceptance Adjudication : exercise the "One-Vote Veto" power to determine whether AI products meet mass-production entry criteria based on acceptance reports, and issue the "AI Productivity Acceptance White Paper" to provide decision-making support for management. Requirements Bachelor's degree or above in Computer Science, Artificial Intelligence, Engineering, Information Systems, Business, or a related discipline. 3+ years of experience in AI, enterprise architecture, software engineering, digital transformation, or technology leadership, with a proven track record of delivering enterprise-scale AI initiatives. Strong understanding of modern AI technologies, including Large Language Models (LLMs), RAG, AI Agents, workflow automation, and enterprise AI platforms. Hands-on experience designing or implementing AI solutions that delivered measurable business outcomes, such as productivity improvement, automation, operational efficiency, or revenue growth. Experience establishing enterprise AI governance, operating frameworks, or AI best practices across business functions. Proficient in deep learning frameworks (PyTorch/TensorFlow) and K8s cloud-native deployment architectures, and strong development skills (Python/Go) to independently write complex stress-testing scripts and data analysis dashboards. Excellent communication and stakeholder management skills, with experience collaborating across executive leadership, business and technology teams. Self-driven, execution-oriented, and comfortable working in a fast-paced environment with evolving priorities. Preferred Qualifications Prior experience within investment management, asset management, hedge funds, trading platforms, fintech, or other regulated financial services. Experience leading enterprise AI transformation, AI governance, or large-scale digital transformation programmes. Familiarity with enterprise AI ecosystems such as Microsoft Copilot, OpenAI, Anthropic, Google Gemini, LangChain, LangGraph, MCP, CrewAI, or similar AI platforms. Knowledge of Responsible AI, model governance, AI risk management, or regulatory compliance. Experience building enterprise AI platforms, AI-native products, or knowledge management solutions. Proven track record of successfully reducing AI inference costs by over 40% is an advantage. Work visa holders are welcome ( QMAS / TTPS / ASMTP Visa). What you’ll get from working at Magic Compass: Competitive compensation and benefits plus discretionary performance bonus Comprehensive Medical insurance coverage Five-days work, bank holiday, Birthday Leave Convenient working locations near MTR Station International exposure in your career and building a global interpersonal network