about the company.
Our client is a prominent statutory authority tasked with ensuring the strength, integrity, and stability of Hong Kong's financial and monetary ecosystems. They are heavily investing in a top-tier digital transformation initiative, scaling up their internal capabilities to embed machine learning and innovation into the core of their operations.
about the team.
...
The AI & Innovation division is an agile, forward-thinking team of specialists driving the adoption of emerging technologies. Operating in a collaborative and highly sophisticated environment, the team focuses on practical research, rapid prototyping, and creating resilient, production-ready systems alongside global technical experts.
about the job.
Guide and mentor a focused team of engineers in building highly scalable AI platform architectures across both on-premise and cloud-native setups.
Establish and mature enterprise MLOps, LLMOps, and AgentOps frameworks to accelerate the end-to-end machine learning lifecycle.
Partner with Data Scientists and AI Engineers to fine-tune model execution, improve latency, and maximise GPU resource efficiency.
Author and maintain Infrastructure as Code (IaC) blueprints to automate the provisioning of core AI development platforms.
Lead evaluation and proof-of-concept (PoC) initiatives for emerging AI tools, frameworks, and foundational open-source models.
Align infrastructure design with organizational security guidelines, ensuring strict adherence to data privacy regulations.
skills & experience required.
University degree in Computer Science, Software Engineering, or a strictly related technical discipline.
Minimum 3 years of dedicated experience in AI/ML platform engineering, backed by a robust background in classic software development.
Deep conceptual grasp of modern AI frameworks, including RAG architectures, model fine-tuning (e.g., LoRA), and evaluation frameworks.
Proven track record in orchestrating data or AI platforms utilising GPU infrastructure and cluster management.
Strong familiarity with data protection frameworks and security best practices regarding LLM deployments.
Fluency in English is mandatory; a background within international teams or the financial services/banking sector is highly advantageous.
preferred technical ecosystem exposure.
Languages & Scripting: Python, SQL, Bash, and PowerShell.
Infrastructure & Orchestration: Docker, Kubernetes, Terraform, and Helm.
DevSecOps & Automation: Jenkins, Git, Ansible, Sonar, Nexus, and Harbor.
Testing & Monitoring: PyTest, jMeter, Grafana, Prometheus, and Loki.
AI Platforms & Engines: MLFlow, Ray, vLLM, Weaviate, Jupyter, and Dify.
Foundational Models: Experience handling open-source models such as Gemma, DeepSeek, Qwen, or GLM.
