Lead DevOps Engineer for AI Projects
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Summary
Lead DevOps Engineer at Oncoustics in Toronto, working full-time on AI/ML pipelines for liver healthcare solutions. The role bridges R&D and software teams, establishes MLOps best practices, and manages end-to-end model deployment to production using tools like Kafka, Cloud Run, and Prometheus.
Join Oncoustics as a Lead DevOps Engineer and revolutionize AI/ML pipelines for liver healthcare solutions. This role involves collaborating with R&D teams and streamlining model deployment processes.
This full-time role in Toronto requires an experienced DevOps or ML engineer dedicated to optimizing the development lifecycle of AI-driven solutions. You will be instrumental in bridging research and software teams, enhancing operational practices, and ensuring efficient model deployment in production settings. Candidates should demonstrate substantial experience with commercial systems and a collaborative spirit.
Key Responsibilities: • Direct the creation of systems for AI experimental efficiency • Facilitate collaboration between R&D and software development • Manage the end-to-end model lifecycle • Establish and lead ML Ops best practices • Guide teams on operational excellence in model deployment
Requirements: • 5+ years of experience in DevOps for cloud applications • Knowledge of tools like Kafka, Cloud Run, and Prometheus • Background in software development and deployment • Strong team collaboration and teaching abilities • Familiarity with machine learning technologies
Bring your DevOps expertise to Oncoustics and contribute to advancements in liver care.
This full-time role in Toronto requires an experienced DevOps or ML engineer dedicated to optimizing the development lifecycle of AI-driven solutions. You will be instrumental in bridging research and software teams, enhancing operational practices, and ensuring efficient model deployment in production settings. Candidates should demonstrate substantial experience with commercial systems and a collaborative spirit.
Key Responsibilities: • Direct the creation of systems for AI experimental efficiency • Facilitate collaboration between R&D and software development • Manage the end-to-end model lifecycle • Establish and lead ML Ops best practices • Guide teams on operational excellence in model deployment
Requirements: • 5+ years of experience in DevOps for cloud applications • Knowledge of tools like Kafka, Cloud Run, and Prometheus • Background in software development and deployment • Strong team collaboration and teaching abilities • Familiarity with machine learning technologies
Bring your DevOps expertise to Oncoustics and contribute to advancements in liver care.