Design, build, and scale AI-driven machine learning infrastructure for modernizing government services. Establish MLOps foundations, optimize pipelines, and collaborate with cross-functional teams to deploy scalable, secure, and high-performing ML models and agentic AI workflows. Shape technical standards, tools, and best practices for production ML systems with significant ownership and impact.
Key Highlights
First MLOps specialist to establish production ML infrastructure and workflows for government AI modernization
Ownership of end-to-end ML pipelines, cloud infrastructure, and CI/CD for model training, deployment, and monitoring
Collaborate with AI researchers, ML engineers, and software teams to transition experimental solutions to production
Key Responsibilities
Design, develop, and maintain end-to-end ML pipelines for model training, evaluation, deployment, and agentic AI workflows
Build and optimize cloud infrastructure (including GPU-based workloads) for scalable model training and serving
Develop and maintain tools for data pipelines, experiment tracking, model performance measurement, and automated retraining
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Technical Skills Required
Python
Kubernetes
Amazon Web Services
Benefits & Perks
Competitive base salary ranging from CAD $150,000 to $200,000 annually
Remote work environment with flexibility and distributed collaboration
Paid time off, health benefits, and professional development opportunities
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Nice to Have
Experience with model optimization techniques (quantization, distillation, TensorRT, ONNX)
Background in distributed systems or high-performance computing
Contributions to open-source projects
Job Description
The Senior MLOps Engineer will design, build, and scale the machine learning infrastructure that powers AI-driven solutions for modernizing government services. As the first MLOps specialist on the team, you will establish the foundations for moving machine learning models and agentic AI workflows from research and experimentation into reliable production environments. This role combines software engineering, machine learning operations, and cloud infrastructure to deliver scalable, secure, and high-performing AI capabilities. You will collaborate closely with AI researchers, ML engineers, and software engineering teams to improve deployment workflows, reproducibility, observability, and operational efficiency. The position offers significant technical ownership and the opportunity to shape MLOps standards, tools, and best practices from the ground up. It is well suited to an experienced engineer who enjoys solving complex infrastructure challenges and enabling teams to deliver impactful AI systems at scale.
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