全职 AI Engineering-AI Ops, Assistant Manager 工作, 薪水, HFG Insurance Recruitment Federal Territory 公司招聘中 - Ricebowl

About the Role

We are seeking an experienced AI Engineering professional to support the delivery of strategic business outcomes through enterprise data and AI solutions.

The AI Ops Engineer will work closely with Data Scientists, Data Engineers, Data Architects and Data Platform teams to design, build, deploy and operate AI/ML and Generative AI solutions. The role will also support local entities in implementing their own data and AI initiatives while ensuring alignment with enterprise technology, infrastructure, data strategy and architecture standards.

A key focus of the role is to strengthen the adoption of AI through scalable cloud platforms, MLOps practices, automation and production-ready AI solutions.

Key Responsibilities

• Collaborate with Data Architects, Data Scientists, Data Engineers and Platform teams to design and deliver scalable data and AI solutions aligned with business and analytical requirements.
• Partner with business stakeholders to develop and implement Generative AI and Agentic AI applications that create business value and improve operational efficiency.
• Work closely with Data Science teams to operationalize AI/ML models and deploy them into production environments.
• Design, implement and maintain MLOps pipelines covering model training, validation, deployment, monitoring and lifecycle management.
• Support the development, optimization, scaling and reliability of AI/ML, Generative AI and data solutions to ensure production readiness and operational excellence.
• Ensure AI and data solutions comply with relevant data governance, security, privacy and regulatory requirements.
• Drive continuous improvement through automation, performance optimization, platform modernization and adoption of emerging AI technologies.
• Provide production support for AI applications, AI models and data pipelines, including incident investigation, root cause analysis, troubleshooting and resolution.
• Maintain comprehensive technical documentation, operational procedures and knowledge repositories.
• Mentor and guide team members while promoting engineering best practices, innovation, collaboration and continuous learning.

Key Performance Indicators

• Achievement of individual and team performance objectives.
• Successful delivery of projects within agreed scope, budget, quality and timeline.
• Availability, reliability and performance of AI and data solutions in production.
• Customer and stakeholder satisfaction.
• Adoption and business impact of delivered AI solutions.
• Continuous improvement through automation and operational efficiency.

Qualifications & Experience

• Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics or a related discipline.
• Minimum 5 years of relevant experience in Data Engineering, MLOps, AI Engineering or related fields.
• Strong analytical thinking, problem-solving and troubleshooting skills.
• Proficiency in Python, Spark and software engineering best practices.
• Hands-on experience developing and deploying Generative AI, LLM-based and Agentic AI applications.
• Familiarity with metadata management.
• Familiarity with AI-assisted development and Vibe Coding tools such as GitHub Copilot, OpenAI Codex and similar technologies.
• Strong understanding of machine learning concepts, model development lifecycle and model deployment practices.
• Experience with cloud platforms such as Microsoft Azure and/or AWS.
• Hands-on experience with Databricks and modern data platforms.
• Experience with MLOps and DevOps tools, CI/CD pipelines, model monitoring and infrastructure automation.

Knowledge & Technical Skills

• Knowledge of modern AI frameworks and ecosystems, including LLM orchestration, prompt engineering, vector databases and agent frameworks, is highly desirable.
• Experience in web application development would be an advantage.
• Experience in the insurance or financial services industry would be an advantage.
• Strong communication and presentation skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Ability to work independently while collaborating effectively across multiple teams and stakeholders.
• Strong ownership mindset, accountability and commitment to delivering high-quality solutions.
• Passion for data, AI, innovation and continuous improvement.

Stakeholder Engagement

Internal:

• Report to the AI Ops Team Leader.
• Collaborate closely with Data Product, Data Science, Data Engineering and Data Platform teams.
• Work with cross-functional stakeholders to deliver enterprise and local data/AI initiatives.

External:

• Engage with digital and data delivery vendors where required.