IT - AI Engineer Lead (Payment & Transaction)

Our client is a large-scale regulated technology platform in Hong Kong seeking a Platform Engineering Lead / AI Platform Lead to drive enterprise AI platform strategy, architecture, governance, and delivery.

This role will lead the design and implementation of scalable, secure, and reliable AI platforms, including LLMs, RAG, agentic workflows, LLMOps / AgentOps standards, and AI-assisted engineering practices.

The successful candidate will work closely with technology leadership, engineering teams, security, governance, and business stakeholders to shape the organisation’s AI roadmap, uplift software engineering productivity, and ensure responsible AI adoption across the enterprise.

Key Responsibilities

AI Strategy & Platform Roadmap

• Define and execute the enterprise AI strategy and roadmap, aligning AI platform initiatives with business objectives and digital transformation priorities.

• Evaluate emerging AI technologies, platforms, and third-party solutions to identify opportunities for innovation, efficiency, and competitive advantage.

• Champion the adoption of AI-assisted software engineering practices to enhance delivery agility and engineering productivity.

AI Platform Architecture & Engineering

• Design, develop, and oversee the implementation of scalable, secure, and reliable AI platforms to support enterprise use cases and agent integration.

• Lead the deployment of advanced AI components including Large Language Models (LLMs), RAG, and agentic workflows.

• Build robust backend systems, application platforms, APIs, and integration patterns to support enterprise-grade AI solutions.

AI-assisted Development & Developer Productivity

• Develop and integrate AI-assisted tools, such as MCP platforms and AI-powered development workflows, into existing software engineering processes.

• Establish best practices for AI-assisted coding, testing, deployment, and developer workflows.

• Improve engineering productivity by enabling practical, controlled, and secure AI tooling adoption across development teams.

LLMOps, AgentOps & Production Reliability

• Establish and maintain LLMOps and AgentOps standards covering model lifecycle management, deployment, monitoring, observability, and quality assurance.

• Implement monitoring and performance controls to ensure AI solutions remain reliable, scalable, and production-ready.

• Drive continuous improvement in platform stability, operational efficiency, and service quality.

Responsible AI Governance & Architecture Standards

• Define enterprise architecture standards, design patterns, and governance frameworks for responsible AI adoption.

• Lead governance forums to ensure AI solutions comply with cybersecurity, regulatory, ethical, data protection, and explainability requirements.

• Establish secure AI deployment standards, model governance practices, and control frameworks for enterprise adoption.

Vendor Evaluation & Stakeholder Engagement

• Evaluate and recommend AI platforms, tools, and third-party solutions.

• Support vendor due diligence and technology assessment activities.

• Partner with senior leadership, business units, and engineering teams to prioritise AI initiatives and promote AI literacy across the organisation.

Requirements

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related discipline.

• Master’s degree in AI, Machine Learning, Data Science, or a related field is preferred.

• Around 10+ years of experience in software engineering, platform engineering, or solution architecture.

• At least 3+ years of hands-on experience in AI, Machine Learning, or Generative AI.

• Proven track record delivering enterprise-grade AI solutions in regulated or large-scale corporate environments.

• Deep hands-on experience with Generative AI, including LLMs, RAG, agentic workflows, and prompt engineering.

• Strong software engineering capability with technologies such as Python, Java, RESTful APIs, and microservices.

• Solid experience designing scalable, secure, and reliable enterprise architectures.

• Familiarity with cloud platforms, LLMOps, AI platform operations, and production-grade backend / application platform design.

• Demonstrated experience establishing AI governance, production monitoring, secure AI deployment standards, model governance, data security, and compliance controls.

• Strong communication and stakeholder influencing skills, including the ability to engage senior stakeholders.

• Self-motivated, hands-on, and passionate about building reliable, scalable, and responsible AI platforms.

Preferred

• Experience in fintech, financial services, regulated technology, or large enterprise environments.

• Exposure to AI-assisted software development, MCP platforms, enterprise developer productivity tools, or AI engineering governance.

• Experience leading architecture forums, technology standards, platform governance, or cross-functional AI delivery initiatives.