AI Engineering Manager

About the role

Sia's Data & AI Business Unit is looking for an AI Engineering Manager to join its team in Hong Kong. The role leads the design and delivery of AI-powered solutions for clients, combining hands-on technical work, solution architecture, consulting delivery and team leadership.

Key responsibilities

• Design, develop and deploy AI solutions for clients across industries, including LLM-powered apps, RAG pipelines, vector databases, semantic search, document intelligence, summarization and workflow automation.
• Build agentic AI systems with tool calling, orchestration, memory management and human-in-the-loop workflows.
• Architect scalable backend systems, APIs, microservices and cloud-native AI applications, and deploy them on AWS, Azure, GCP, Alibaba Cloud or Tencent Cloud.
• Drive MLOps and GenAIOps, including CI/CD, testing, monitoring, evaluation and production readiness.
• Work with clients on workshops, discovery sessions and architecture reviews, and advise on AI adoption, governance and responsible AI.
• Lead small to medium engineering teams, mentor engineers and coordinate with data science, product, cloud and security teams.

Required profile

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Software Engineering or a related field.
• 6+ years of experience in AI engineering, MLOps, GenAIOps, data engineering or applied AI delivery.
• Hands-on experience delivering AI/ML solutions in production or production-like environments.
• Consulting, client-facing or professional services experience strongly preferred.
• Fluency in English; Cantonese highly preferred, Mandarin or another Asian language advantageous.

Required skills

• Python and modern AI/ML frameworks such as PyTorch, Hugging Face, LangChain, LlamaIndex and n8n.
• Cloud AI services such as AWS/Bedrock, Azure/Foundry, GCP/Vertex, Databricks or similar.
• Backend engineering, APIs, microservices, containerization and cloud-native architecture.
• MLOps or GenAIOps practices (CI/CD, monitoring, model evaluation).
• Preferred: Docker, Kubernetes, Terraform, agent frameworks, AI-native tools (GitHub Copilot, Cursor, Claude Code) and experience leading technical teams.