Senior MLOps Engineer

Clariti Cloud Inc.

5h•CA$150,000 - CA$200,000•Remote

About The Position

The Senior MLOps Engineer will design, build, and scale the systems that power CivCheck and Clariti’s AI capabilities. As the first MLOps Engineer, this person will lead development of robust machine-learning infrastructure, helping move models from research into production reliably, observably, and at scale. The role sits at the intersection of machine learning, software engineering, and cloud infrastructure, enabling teams to deliver high-impact ML systems efficiently and safely.

Requirements

6–10+ years of experience in software or ML engineering, including at least 3+ years in MLOps or ML infrastructure.
Solid experience with Python, C, C++, Bash, or similar languages.
Proven experience deploying and managing ML models in production.
Proficiency with Docker and Kubernetes for scalable ML system design.
Experience with cloud platforms such as AWS, GCP, or Azure and GPU orchestration.
Hands-on knowledge of CI/CD pipelines, such as GitHub Actions, Jenkins, or similar.
Familiarity with MLflow, Weights & Biases, Kubeflow, or similar tools for experiment tracking and pipeline automation.
Solid understanding of data versioning, model reproducibility, and monitoring strategies.
Excellent problem-solving skills and a collaborative, team-oriented mindset.

Nice To Haves

Experience training models from scratch, including defining architectures, curating and cleaning datasets, tuning training parameters, and taking models from research to monitored production.
Exposure to model optimization techniques such as quantization, distillation, TensorRT, and ONNX.
Familiarity with infrastructure-as-code tools such as Terraform and CloudFormation.
Background in distributed systems or high-performance computing.
Contributions to open-source projects.

Responsibilities

Design and maintain end-to-end ML pipelines for model training, evaluation, deployment, and agentic AI workflows.
Build and optimize infrastructure for training and model serving across cloud providers and compute instances.
Develop tools for data pipelines, data labeling, experiment and performance tracking, and automated retraining.
Collaborate with AI researchers and ML engineers to productionize proofs of concept and ensure model reproducibility and scalability.
Implement CI/CD best practices for ML systems, including continuous integration, automated testing, and deployment workflows.
Monitor and manage model health, performance, drift, and data quality in production.
Partner with Engineering teams to streamline infrastructure provisioning and data access.
Drive cost optimization and performance tuning for large-scale model training.
Contribute to internal documentation and best practices.