AI Engineer - Manager

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field.
• 6+ years of relevant experience in AI engineering, MLOps, DevOps, GenAIOps, data engineering, or applied AI delivery.
• Experience working in a consulting, client-facing, or professional services environment is strongly preferred.
• Hands-on experience designing and delivering AI/ML solutions in production or production-like environments.
• Strong programming skills in Python and experience with modern AI/ML frameworks and techniques such as PyTorch, Hugging Face, LangChain, LlamaIndex, and AI orchestration platform such as n8n.
• Experience with cloud-based AI services and platforms, such as AWS/Bedrock, Azure/Foundry, GCP/Vertex, Alicloud/PAI, Tencent Cloud/TI, Databricks, or similar providers.
• Strong understanding of backend engineering, APIs, microservices, containerization, and cloud-native architecture.
• Familiarity with MLOps or GenAIOps practices, including CI/CD, monitoring, model evaluation, observability, and production support.
• Excellent communication skills with the ability to explain complex technical concepts to senior stakeholders, business users, and technical teams.
• Strong problem-solving ability, business acumen, and comfort working in ambiguous client environments.
• Fluency in English is required. Fluency in Cantonese is highly preferred for the Hong Kong market. Mandarin or other Asian language proficiency would be advantageous.
• Experience leading technical teams or managing AI engineering workstreams.
• Experience delivering enterprise AI solutions across sectors such as banking, insurance, energy, or retail & consumer goods.
• Experience with agent frameworks, , multi-agent orchestration, memory architectures, and workflow automation, on-prem and on cloud LLM deployments
• Experience with Docker, Kubernetes, Terraform, CI/CD pipelines, and modern DevOps practices.
• Familiarity with security, data privacy, model risk management, Responsible AI, GDPR, SOC2, or AI governance frameworks.
• Experience using AI-native engineering tools su

Additional Information

We are looking for an AI Engineering Manager to join our team in Hong Kong to help lead the design and delivery of AI-powered solutions for our clients. This role combines hands-on technical expertise, designing solution architecture, consulting delivery, and team leadership. AI Solution Design & Delivery Lead the design, development, and deployment of AI solutions for clients across multiple industries. Build applications powered by LLMs, RAG pipelines, vector databases, semantic search, document intelligence, summarization, and workflow automation. Design and implement agentic AI systems, including tool calling, orchestration, memory management, and human-in-the-loop workflows. Ensure AI solutions are aligned with client objectives, performance requirements, security standards, and cost considerations. Engineering & Architecture Provide technical leadership across the AI engineering lifecycle, from prototyping through to production deployment. Architect scalable backend systems, APIs, microservices, and cloud-native AI applications. Work with frameworks and tools such as Python, LangChain, LlamaIndex, Hugging Face, PyTorch, FastAPI, Flask, Docker, and Kubernetes. Deploy industry-standard AI solutions on cloud platforms such as AWS, Azure, GCP, Alibaba Cloud, or Tencent Cloud. MLOps, GenAIOps & Production Readiness Drive MLOps and GenAIOps best practices, including CI/CD, testing, monitoring, evaluation, and production support. Establish evaluation frameworks for LLM applications, including relevance, explainability, factuality, hallucination risk, latency, cost, and robustness. Ensure AI systems are production-ready, scalable, secure, maintainable, and aligned with operational requirements and industry standards. Consulting & Client Leadership Work directly with clients to understand business challenges, identify AI opportunities, and translate requirements into solution designs. Lead client workshops, technical discovery sessions, architecture reviews, and solution demonstrations. Communicate complex AI and technical concepts clearly to both technical and non-technical stakeholders. Support sales, pre-sales, and proposal development by shaping technical solutions, delivery plans, estimates, and narratives. Advise clients on AI adoption, responsible AI, operating models, governance, and technology strategy. Team Leadership & Delivery Management Lead small to medium-sized engineering teams in the delivery of AI and GenAI projects. Mentor AI engineers, software engineers, and junior consultants through technical guidance, code reviews, and structured feedback. Coordinate across data science, engineering, product, cloud, security, and business teams to ensure successful delivery. Promote engineering excellence, documentation, reusable assets, knowledge sharing, and continuous improvement.

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