AI Engineer - Full time
Indexed description
Posting ID: 7920
Status: Permanent Full Time
Department: AI & Digital Planning
Role Level: Professional Group PG10 ($45.02 to $56.28)
Posted Date: July 9, 2026
Posting deadline: July 26, 2026
A New Kind of Health Care for a Healthier Community. That’s our Vision at Trillium Health Partners (THP), one of the largest community-based acute care facilities in Canada. Comprised of the Credit Valley Hospital, the Mississauga Hospital and the Queensway Health Centre, along with several satellite locations, THP serves the growing and diverse populations of Mississauga, West Toronto and surrounding communities, and is a teaching hospital affiliated with the University of Toronto and home to Institute for Better Health.
Our common purpose is to improve care, elevate health, and strengthen the communities we serve. Grounded in the values of compassion, excellence, and courage, we strive each day to be our best and work toward a better future. Our mission begins with delivering the highest quality care through continuous improvement, while partnering to create a more seamless health system.
We provide services centred on people’s needs, striving for better health by supporting prevention as well as delivering treatment. We help communities thrive by building partnerships, fostering inclusion, and strengthening health and well-being for all.
Learn more about THPs Strategy by reading our Plan to 2030 .
Aligned to our Plan to 2030, THP is working together with our community on Trillium HealthWorks, the largest infrastructure renewal in Canada’s history, that will shape the future of our community and beyond, transforming health care with the future Peter Gilgan Mississauga Hospital, including Ontario’s first hospital dedicated to women’s and children’s health at the Shah Family Hospital for Women and Children and expanding the Queensway Health Centre, which will become The Gilgan Family Queensway Health Centre.
Learn more about Trillium HealthWorks by visiting the Trillium HealthWorks Website.
If you are passionate about your career, motivated to improve the health of the community, committed to excellence, quality and patient safety consider joining our Better Together team!
Job Description
Reporting to the Director, AI & Digital Planning, the AI Engineer will be responsible for using the latest AI tools to design, prototype, build, evaluate, and scale AI solutions including Generative and Agentic AI, that will be deployed and sustained across the enterprise with the lens of user experience, human-centred design, and product and lifecycle management. This role will advance Trillium HealthWorks priorities through the operationalization of AI within key priority projects including the Trillium Operations Centre and Trillium Connect, designing future systems that will be used in our foundational infrastructure projects.
Key Responsibilities
- Design, build, and deploy intelligent agents and multi-agent systems that augment clinical and operational workflows, spanning task automation, decision support, and end-to-end process orchestration, using cloud AI platforms (e.g., Azure AI Foundry, Copilot Studio) and open-source frameworks
- Collaborate with internal teams including Information Systems and Business Intelligence to develop AI solutions, using best practices for engineering, designing future systems for Trillium HealthWorks
- Embed directly within clinical and operational teams to identify automation and agentic opportunities, integrate AI solutions into their specific workflows, and streamline day-to-day processes.
- Manage the end-to-end lifecycle of AI solutions from design and development, to implementation and sustainability, using a product design mindset.
- Develop API endpoints and implement Model Context Protocols (MCPs) for exchanging healthcare information and connecting AI agents to external tools and data sources.
- Connect AI agents to core hospital systems (EHR, HRIS, ERP) via APIs to allow agents to perform actions, not just retrieve information.
- Contribute to the design and implementation of techniques to optimize AI performance and grounding, including retrieval-augmented generation (RAG) pipelines, structured output enforcement, prompt engineering and versioning, and few-shot/chain-of-thought strategies to ensure AI solutions are accurate, context-aware, and aligned with clinical and operational data sources.
- Build and maintain reliability and safety infrastructure for AI systems in a healthcare environment under the guidance of senior team members, including guardrails for input/output filtering and PHI protection, systematic evaluation and benchmarking frameworks (hallucination detection, clinical accuracy, bias testing), and human-in-the-loop patterns that define appropriate escalation thresholds and clinician review points.
- Contribute to the design and maintenance of agentic orchestration workflows, including multi-agent coordination with state management and context passing, branching logic with error handling and fallback patterns, and observability/tracing infrastructure to support audit trails and regulatory compliance.
- Ensure AI solutions comply with privacy, security, and responsible AI requirements, including PHIPA and organizational governance frameworks
- Monitor and troubleshoot production issues to maintain high availability and reliability.
- BS in computer science, engineering or a similar discipline, or equivalent practical experience
- 2-4 years in software engineering, AI engineering, or related role
- Python proficiency
- Experience building LLM-powered systems
- Experience with at least one orchestration framework (LangGraph, LangChain, Semantic Kernel, or equivalent)
- Experience with API design and integration
- Familiarity with version control (Git) and CI/CD practices
- Strong communication and collaboration skills to work effectively with cross-functional teams and customers
- Ability to troubleshoot complex issues and identify root causes
- Master's degree in Computer Science or a related technical field
- Familiarity with agentic system design
- Familiarity with MCP
- Exposure to RAG pipeline architecture
- Interest in healthcare AI
- Familiarity with agentic and GenAI evaluation practice
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