Senior AI Engineer

Zurich Services (Hong Kong) Limited; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Zurich Services (Hong Kong) Limited is seeking a hands-on AI Engineer to turn business problems into secure, scalable AI-enabled solutions. You will design, build, ship and continuously improve AI products that integrate with enterprise data and APIs.

The role emphasizes ownership from discovery through production, collaboration with business, product, data and technology teams, and strengthening engineering practices in a growing AI team.

Qualifications

• 5-8 years of hands-on software, data or AI engineering experience with production delivery.
• Strong Python engineering skills and backend services & APIs.
• Hands-on experience with FastAPI or a similar Python API framework.
• Practical GenAI/LLM delivery including RAG pipelines or AI agents.
• Experience with cloud delivery on Azure or AWS and containerized environments.
• Understanding of secure, scalable software and enterprise data integration.

Responsibilities

• Build and ship AI-enabled applications from prototype to production with quality and reliability.
• Design applied AI architectures including LLMs, agents and retrieval workflows.
• Develop robust backend services, scalable APIs and asynchronous processing.
• Integrate AI services with enterprise data sources, APIs and user interfaces.
• Implement testing, monitoring, guardrails and security controls for trust.
• Operate and optimize performance, reliability, latency and cost after deployment.
• Contribute to CI/CD, infrastructure-as-code and engineering patterns.
• Collaborate across business, product and technology teams to uplift the team.

Skills

Python

LLM / GenAI

Async processing

RAG pipelines

CI/CD

Azure / AWS

APIs / microservices

Tools

FastAPI

Job description

We are looking for a hands‑on AI Engineer who can turn business problems into secure, reliable and scalable digital solutions. You will design, build, ship and continuously improve AI-enabled products that integrate with enterprise data, APIs and operational systems.

This role suits a practical builder with the right mindset and attitude: curious, accountable, collaborative and comfortable owning delivery from early discovery through production. You will work closely with business, product, data and technology colleagues, while helping strengthen the engineering practices of a growing AI team.

Your Role

• Build and ship production solutions. Develop AI-enabled applications and services from prototype to production, with ownership of quality, reliability and ongoing improvement.
• Design applied AI architectures. Build LLM applications, AI agents, retrieval‑augmented generation services, document‑intelligence workflows and API‑based integrations using fit‑for‑purpose patterns.
• Develop robust backend services. Write production‑grade Python, design scalable APIs and microservices, and implement asynchronous processing for long‑running or high‑volume workloads.
• Integrate the digital stack. Connect models and AI services with enterprise platforms, data sources, workflow systems, user interfaces and upstream or downstream applications.
• Engineer for trust. Implement automated testing, evaluation, monitoring, guardrails, security controls, human review and fallback paths to manage accuracy and failure modes.
• Operate what you build. Monitor performance, reliability, latency and cost; troubleshoot issues and improve solutions after deployment.
• Strengthen delivery practices. Contribute to code reviews, CI/CD, infrastructure‑as‑code, technical documentation and reusable engineering patterns.
• Collaborate and uplift the team. Explain technical trade‑offs clearly, support less‑experienced engineers and work constructively across business and technology teams.

Your Skills and Experience

• 5-8 years of hands‑on software, data or AI engineering experience, including demonstrable delivery of digital products or platforms into production.
• Strong Python engineering skills and experience developing maintainable backend services, APIs and microservices.
• Hands‑on experience with FastAPI or a comparable Python API framework.
• Practical experience delivering GenAI or LLM solutions, such as RAG pipelines, AI agents, document intelligence or workflow automation.
• Experience with orchestration frameworks such as LangGraph, LangChain, LlamaIndex or equivalent.
• Understanding of retrieval design, embeddings, vector databases and search quality, including chunking, metadata, hybrid search or reranking.
• Experience with asynchronous processing and queuing patterns using tools such as Azure Service Bus, Celery, Redis or equivalent.
• Experience with cloud‑native delivery on Azure or AWS, containers, CI/CD and infrastructure‑as‑code.
• Working knowledge of SQL database, application monitoring, automated testing and version control.
• Understanding of privacy, security, data governance and responsible AI considerations in an enterprise environment.
• Strong problem‑solving and communication skills, with the ability to translate business needs and architectural trade‑offs into practical delivery decisions.

The Mindset We Value

• Builder mentality. You prefer working software and measurable outcomes over purely conceptual designs.
• Ownership. You follow through from problem definition to deployment, adoption and continuous improvement.
• Pragmatism. You select the simplest fit‑for‑purpose approach and know when conventional software or human review is more appropriate than AI.
• Learning agility. You keep pace with a rapidly evolving AI landscape without chasing tools for their own sake.
• Collaboration. You seek feedback, communicate openly and help the wider team succeed.
• Resilience. You are comfortable navigating ambiguity, production issues and changing priorities with a constructive attitude.

Why Join Us

You will have meaningful ownership of applied AI solutions and the opportunity to shape how they are engineered and operated in a complex enterprise environment. The role combines hands‑on building, real business problems and close collaboration with a developing team that is moving from experimentation towards production‑grade delivery.