TECHNICAL SPECIALIST, MLOps ENGINEER
Toronto, ON, Canada
University Health Network
The University Health Network (UHN), consisting of Princess Margaret Cancer Centre, Toronto General Hospital, Toronto Western Hospital and Toronto Rehabilitation Institute, is a recognized leader in patient care, research and education.Company Description
UHN is Canada’s #1 hospital and the world’s #1 publicly funded hospital. With 10 sites and more than 44,000 TeamUHN members, UHN consists of Toronto General Hospital, Toronto Western Hospital, Princess Margaret Cancer Centre, Toronto Rehabilitation Institute, The Michener Institute of Education and West Park Healthcare Centre. As Canada's top research hospital, the scope of biomedical research and complexity of cases at UHN have made it a national and international source for discovery, education and patient care. UHN has the largest hospital-based research program in Canada, with major research in neurosciences, cardiology, transplantation, oncology, surgical innovation, infectious diseases, genomic medicine and rehabilitation medicine. UHN is a research hospital affiliated with the University of Toronto.
UHN’s vision is to build A Healthier World and it’s only because of the talented and dedicated people who work here that we are continually bringing that vision closer to reality.
Job Description
Union: Non-Union
Site: 200 Elizabeth St, Toronto, ON M5G 2C4
Department: AI Collaborative Centre
Reports to: Chief AI Scientist
Work Model: Hybrid (3 days on site)
Hours: 37.5
Salary: $87,107.00 to $108,888.00 annually (to commensurate with experience and consistent with UHN compensation policy)
Shifts: Monday to Friday
Status: Temporary Full-Time
Closing Date: May 13, 2025
Position Summary:
Join the forefront of AI-driven health care innovation as a Technical Specialist, MLOps Engineer at the University Health Network (UHN) AI Hub, where cutting-edge technology meets life-changing impact. Work alongside top scientists, clinicians, and researchers to design and develop sophisticated ML pipelines, agent systems, and deployment platforms, driving advancements in cancer care and beyond. In this dynamic role, you’ll apply MLOps methodologies to solve complex health challenges, optimize hospital operations, and pioneer groundbreaking AI solutions. If you're passionate about transforming data into responsible AI-driven breakthroughs, this is your opportunity to lead the way!
Duties:
- Develop and manage data processing, ML pipelines and agent systems to support AI-driven health care innovations. This involves pre-processing, cleaning, and organizing multi-modal data for data pipelines and AI integration.
- TECDeploy, monitor, and optimize machine learning models in clinical environments and processes to ensure seamless integration and efficiency. Set up monitoring tools to monitor AI performance and track various metrics, such as response time, errors and resource utilization.
- Collaborate with clinicians and researchers to design secure data pipelines and implement responsible AI applications. Regularly document your findings and methodologies. Publish in peer-reviewed journals and present at conferences.
- Apply MLOps methodologies to enhance hospital efficiency, streamline workflows, and improve patient care.
- Ensure compliance with ethical AI standards and contribute to the development of safe and responsible AI practices in healthcare.
- Provide technical expertise and mentorship to research teams, students, and clinicians working on AI-related projects.
- Stay ahead of emerging AI trends by continuously learning and integrating cutting-edge technologies into UHN’s AI ecosystem.
Qualifications
- Undergraduate degree, graduate would be an asset, in engineering, computer science, or related disciplines.
- Minimum 5 years related experience required.
- Excellent communication skills and ability to utilize a collaborative approach to solving challenging problems.
- Experience with MLOps, enterprise application development, implementing and maintaining ML and NLP models, IT administration and support.
- Experience with Python (scikit-learn, Pandas, etc.) and deep learning frameworks (TensorFlow or PyTorch).
- Experience with healthcare data types, topics, and scientific challenges and approaches.
- Familiarity with HPC environment and running applications in HPC clusters.
- Demonstrate ability to mentor others and work collaboratively with clinicians.
Additional Information
Why join UHN?
In addition to working alongside some of the most talented and inspiring healthcare professionals in the world, UHN offers a wide range of benefits, programs and perks. It is the comprehensiveness of these offerings that makes it a differentiating factor, allowing you to find value where it matters most to you, now and throughout your career at UHN.
- Competitive offer packages
- Government organization and a member of the Healthcare of Ontario Pension Plan (HOOPP https://hoopp.com/)
- Close access to Transit and UHN shuttle service
- A flexible work environment
- Opportunities for development and promotions within a large organization
- Additional perks (multiple corporate discounts including: travel, restaurants, parking, phone plans, auto insurance discounts, on-site gyms, etc.)
Current UHN employees must have successfully completed their probationary period, have a good employee record along with satisfactory attendance in accordance with UHN's attendance management program, to be eligible for consideration.
All applications must be submitted before the posting close date.
UHN uses email to communicate with selected candidates. Please ensure you check your email regularly.
Please be advised that a Criminal Record Check may be required of the successful candidate. Should it be determined that any information provided by a candidate be misleading, inaccurate or incorrect, UHN reserves the right to discontinue with the consideration of their application.
UHN is an equal opportunity employer committed to an inclusive recruitment process and workplace. Requests for accommodation can be made at any stage of the recruitment process. Applicants need to make their requirements known.
We thank all applicants for their interest, however, only those selected for further consideration will be contacted.
Tags: Computer Science Data pipelines Deep Learning Engineering HPC Machine Learning ML models MLOps NLP Pandas Pipelines Python PyTorch Research Responsible AI Scikit-learn TensorFlow
Perks/benefits: Career development Competitive pay Conferences Flex hours Health care
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