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Vista previa de la oferta

AI Application Engineer

Technology / Software Development

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:

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:

AI Application Engineer