Position: MLOps Developer
Location: Toronto, 2 days onsite (12 minutes by car from Union Station)
Salary: Up to $155K
Posting Type: Open vacancy
We are seeking an experienced MLOps / Machine Learning Platform Engineer to design, develop, and maintain end-to-end automation across the AI/ML lifecycle within a GCP cloud environment. This role will focus on building and optimizing ML infrastructure, developing CI/CD pipelines for machine learning models, and automating data processing, model training, deployment, and serving.
What you will do: Own End-to-End Automation.
Develop automated tools for all stages of the AI/ML pipeline in a cloud environment, spanning data processing, model training, and serving.
Design and maintain CI/CD pipelines tailored for ML artifacts (data, models, and code).
Work with DevOps team to provision, manage, and scale cloud-based infrastructure (primarily GCP) to support high-throughput inference.
Optimize deep learning models for seamless deployment and performance across web and mobile platforms.
Collaborate closely with ML engineers and product teams to convert domain-specific APIs into structured, LLM-controllable tools.
Conduct peer code reviews, automated testing, and promote agile methodology.




Requirements: 3+ years of hands-on experience as a MLOps "developer" in a an IaC (Infrastructure as Cloud) development environment
Strong automation and programming skills in Python to develop automation tools and workflows
Experience with GCP cloud (MUST-HAVE), NOT AWS and NOT Azure
Experience with Data Version Control Tools (DVC) such as MLFlow
Experience with Docker container orchestration and Kubernetes
Solid ML fundamentals: understanding how models are trained, versioned and deployed
End-to-end ML pipeline ownership, including maintenance, reliability and cost optimization
Solid engineering practices and familiarity with modern tooling
Education: Education in a technical field
Ideal Candidate Profile: Someone from a startup or mid-sized organization who has worked across infrastructure, ML pipelines and deployment, rather than a narrowly specialized engineer from a large MLOps team.
All applications are reviewed by our recruitment team, and hiring decisions are made by people. We may also use AI-enabled tools to support parts of the application review process.

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📌 MLOps Developer (Ontario)
🏢 Quantum Technology Recruiting Inc. (QTR)
📍 Ontario