Advancing AI Innovation Through Cross-Functional Collaboration (3)

Cole Meikle will work alongside the Health Team on Primary and Preventative Health Services (PPHS) project.
Project Description: As part of the Health Team, Cole will work under the guidance of senior developers to support the design and evaluation of agentic workflows tailored for Primary and Preventative Health Services (PPHS). This collaborative effort involves exploring how autonomous AI agents and machine learning can be integrated into healthcare environments to improve operational efficiency, specifically focusing on complex domains like Grant Review and Workforce Planning.
Dominic Mao will work alongside the Health Team on the Primary and Preventative Health Services (PPHS) project
Project Description: As part of the Health Team, Cole will work under the guidance of senior developers to support the design and evaluation of agentic workflows tailored for Primary and Preventative Health Services (PPHS). This collaborative effort involves exploring how autonomous AI agents and machine learning can be integrated into healthcare environments to improve operational efficiency, specifically focusing on complex domains like Grant Review and Workforce Planning.
Philip William Ventura will work alongside the Health Team on the Primary and Preventative Health Services (PPHS) project
Project Description: As part of the Health Team, the Full-Stack ML Developer will work under the guidance of senior developers to support the design, training, and evaluation of machine learning models and agentic workflows tailored for Primary and Preventative Health Services (PPHS). This collaborative effort involves exploring how autonomous AI agents and predictive modeling can be integrated into healthcare environments to improve operational efficiency, specifically focusing on complex domains like Grant Review and Workforce Planning.
Eric Hu, Krishna Jawale, Nirvan Rabbani, and Ruby Zhou will join the Public Sector team. Unlike traditional single-project placements, these interns will operate as a cross-functional technical cohort supporting a diverse portfolio of Government of Alberta initiatives. This flexible model allows the team to deploy the interns across multiple high-priority use cases, such as document classification, workforce optimization, or policy research, depending on immediate operational needs and the stage of the project lifecycle.
The innovation challenge centers on integrating three core technical pillars into public service workflows:
? Agentic AI: Developing autonomous workflows to handle complex decision-support tasks.
? Retrieval-Augmented Generation (RAG): Implementing intelligent search and interaction layers for large-scale government policy repositories.
? Classical Machine Learning: Building predictive models for forecasting staffing demands or service utilization.
Government of Alberta Initiatives include:
1. Wildfire Workforce Optimization: Interns will develop predictive models and agentic agents to optimize Duty Officer assignments. The goal is to reduce burnout risk and overtime costs through better decision support for wildfire leadership.
2. Research Assistant SWAT Team: Supporting the rollout of a production-ready AI product, the interns will join a dedicated team to streamline the deployment of RAG-based search and interaction layers across various GoA partners to accelerate program delivery.
3. Income Support Program Integrity: The cohort will deploy document intelligence and risk-scoring models to handle high application volumes. This initiative uses AI to rapidly flag potential fraud while fast-tracking low-risk applications to shorten decision timelines for vulnerable residents.
The anticipated economic and social benefits include significant cost savings through reduced manual administrative burdens and improved accuracy in resource planning. By modernizing information management and decision workflows, this project directly enhances the efficiency, transparency, and scalability of essential public services provided to Albertans.