Senior AI Application Engineer

Key Responsibilities:

• Facilitate LLM API (e.g., GPT, Claude) for enterprise use cases.
• Collaborate with business team and data team to integrate GenAI into workflows such as eClaim, OCR, and QA automation.
• Develop AI-driven code review tools to improve documentation, logic traceability, and reduce technical debt.
• Integrate AI classifiers for email triage and document categorization using LLMs and rule-based systems.
• Develop text2sql solution with LLM.
• Optimize prompt engineering strategies for accuracy, relevance, and performance.
• Implement secure and scalable inference pipelines using Azure or AWS.
• Monitor model outputs for bias, hallucination, and compliance with Zurich’s security standards.
• Ensure AI applications comply with Zurich’s data protection and privacy policies, including handling of PII and consented data.
• Support GenAI workshops and internal enablement initiatives to promote AI adoption.

Your Skills and Experience

• Bachelor’s or Master’s in Computer Science, AI Engineering, or related field.
• Experience in deploying AI models into production environments.
• Proficiency in Python, TensorFlow, PyTorch, and Langchain framework.
• Familiarity with LLMs (e.g., GPT, Claude, LLaMA) APIs, prompt tuning, and GenAI deployment.
• Strong understanding of enterprise application architecture and integration points.
• Knowledge of data privacy, compliance, and ethical AI standards.
• Familiarity with DevOps tools and containerization technologies, such as Jenkins, Azure Pipeline, GitHub Actions, Docker and Kubernetes.
• Proficiency with Git and GitHub workflows.
• Knowledge of cloud platforms and services, such as AWS and Azure, as well as experience in managing cloud-based infrastructure.
• Strong analytical and problem-solving skills.
• Excellent communication, documentation and collaboration skills, as well as the ability to work effectively in cross-functional teams.
• A deep understanding of agile methodologies and principles.
• Familiarity with security best practices and the ability to implement security measures in the software development lifecycle.
• A commitment to continuous learning and staying up-to-date with the latest industry trends and technologies.
• Self-motivated, proactive and responsible.
• Proficiency in English reading and writing skills, and the ability to use either English or Cantonese speaking as a working language.

Having one or more of the following is a plus:

• Prior work on AI-enhanced claims processing, QA automation, or document triage.
• Experience with Copilot or similar GenAI platforms.
• Participation in GenAI workshops or pilot programs.

Key Responsibilities:

• Facilitate LLM API (e.g., GPT, Claude) for enterprise use cases.
• Collaborate with business team and data team to integrate GenAI into workflows such as eClaim, OCR, and QA automation.
• Develop AI-driven code review tools to improve documentation, logic traceability, and reduce technical debt.
• Integrate AI classifiers for email triage and document categorization using LLMs and rule-based systems.
• Develop text2sql solution with LLM.
• Optimize prompt engineering strategies for accuracy, relevance, and performance.
• Implement secure and scalable inference pipelines using Azure or AWS.
• Monitor model outputs for bias, hallucination, and compliance with Zurich’s security standards.
• Ensure AI applications comply with Zurich’s data protection and privacy policies, including handling of PII and consented data.
• Support GenAI workshops and internal enablement initiatives to promote AI adoption.

Your Skills and Experience

• Bachelor’s or Master’s in Computer Science, AI Engineering, or related field.
• Experience in deploying AI models into production environments.
• Proficiency in Python, TensorFlow, PyTorch, and Langchain framework.
• Familiarity with LLMs (e.g., GPT, Claude, LLaMA) APIs, prompt tuning, and GenAI deployment.
• Strong understanding of enterprise application architecture and integration points.
• Knowledge of data privacy, compliance, and ethical AI standards.
• Familiarity with DevOps tools and containerization technologies, such as Jenkins, Azure Pipeline, GitHub Actions, Docker and Kubernetes.
• Proficiency with Git and GitHub workflows.
• Knowledge of cloud platforms and services, such as AWS and Azure, as well as experience in managing cloud-based infrastructure.
• Strong analytical and problem-solving skills.
• Excellent communication, documentation and collaboration skills, as well as the ability to work effectively in cross-functional teams.
• A deep understanding of agile methodologies and principles.
• Familiarity with security best practices and the ability to implement security measures in the software development lifecycle.
• A commitment to continuous learning and staying up-to-date with the latest industry trends and technologies.
• Self-motivated, proactive and responsible.
• Proficiency in English reading and writing skills, and the ability to use either English or Cantonese speaking as a working language.

Having one or more of the following is a plus:

• Prior work on AI-enhanced claims processing, QA automation, or document triage.
• Experience with Copilot or similar GenAI platforms.
• Participation in GenAI workshops or pilot programs.