At AIA we’ve started an exciting movement to create a healthier, more sustainable future for everyone.
As pioneering innovators for over 100 years, we’re now transforming our organisation to be faster, simpler and more connected. Because we want to be even better equipped to develop digital solutions and experiences that help more people live Healthier, Longer, Better Lives.
To get there, we need people with tech/digital/analytics expertise and passion to help develop positive, sustainable change through digitally enhanced experiences that will impact the lives of millions of people and create a healthier future for everyone.
If you believe in developing a better tomorrow, read on.
About the Role
The role involves designing, developing, and implementing advanced generative models using cutting-edge AI technologies to achieve business objectives.
AI/ML consultant partners with business stakeholders in leveraging artificial intelligence and machine learning technologies to identify areas where AI/ML can bring improvements, such as automating processes, improving customer experiences, or gaining insights from data.

• Design, develop, and implement advanced generative models using cutting-edge AI technologies, and maintain machine learning and deep learning models.
• Research and integrate mature AI solutions from the market (e.g., OpenAI, Google AI) to enhance the app’s intelligence, improving service efficiency and user experience.
• Select and implement appropriate AI APIs/models based on business requirements, optimizing applications in areas such as NLP, computer vision, and personalized recommendations.
• Design, develop, and implement AI models and algorithms for NLP and Computer Vision applications.
• Collaborate with data scientists and engineers to design and implement machine learning pipelines using MLOps practices for Data Preparation, Model Training/Re-Training, Deployment, Inference and Monitoring.
• Develop and deploy scalable REST APIs to expose ML models to various business applications for online inference.
• Develop and implement data pipelines for curation of structured/unstructured and semi-structured data for Feature Engineering, Model Training and Batch/Online Inference.
• Conduct research and development activities to improve the performance and efficiency of generative models.