Senior MLOps Engineer

What you'll do

• Build and maintain ML pipelines for training, evaluation, deployment, and agentic AI workflows.
• Develop and optimize infrastructure for model training and serving, data pipelines, experiment tracking, and automated retraining.
• Productionize models with AI and engineering teams; implement CI/CD, monitor model health, and optimize cost and performance.

What they ask for

• 6–10+ years of software or ML engineering experience, including at least 3+ years in MLOps or ML infrastructure.
• Experience with Python, C, C++, Bash, or similar, and deploying and managing ML models in production.
• Proficiency with Docker and Kubernetes; experience with AWS, GCP, or Azure and GPU orchestration.
• Hands-on CI/CD experience and understanding of data versioning, model reproducibility, and monitoring.

Nice to have

• Experience training models from scratch and taking them from research to monitored production.
• Exposure to quantization, distillation, TensorRT, ONNX, infrastructure-as-code, distributed systems, high-performance computing, or open-source contributions.

Pay

$150-200k CAD (base salary)

Tools

Python, C, C++, Bash, Docker, Kubernetes, AWS, GCP

AI focus

Builds and operates ML infrastructure for AI capabilities, including model training, evaluation, serving, agentic AI workflows, productionization, monitoring, and retraining.