Senior MLOps Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior MLOps Engineer based in United States .

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.

• Design, develop, and maintain end-to-end machine learning pipelines for model training, evaluation, deployment, and agentic AI workflows.

• Build and optimize infrastructure for model training and serving across cloud platforms and compute environments, including GPU-based workloads.

• Develop and maintain tools supporting data pipelines, data labeling, experiment tracking, model performance measurement, and automated retraining.

• Collaborate with AI researchers and machine learning engineers to transition proof-of-concept solutions into production-ready systems that are reproducible, reliable, and scalable.

• Implement and improve CI/CD practices for machine learning systems, including continuous integration, automated testing, and deployment workflows.

• Monitor production models for performance, health, data quality, and model drift, implementing appropriate observability and monitoring strategies.

• Partner with engineering teams to streamline infrastructure provisioning, improve data access, and strengthen the reliability of shared technical systems.

• Optimize infrastructure costs and system performance for large-scale model training and inference workloads.

• Establish and maintain documentation, technical standards, and best practices for MLOps workflows and infrastructure management.

• Contribute to the development of scalable, maintainable, and efficient systems that enable AI capabilities to be delivered safely and consistently.

Requirements:

• 6–10+ years of experience in software engineering or machine learning engineering, including at least 3 years focused on MLOps or machine learning infrastructure.

• Strong programming skills in Python and experience with languages and scripting tools such as C, C++, and Bash.

• Demonstrated experience deploying, operating, and maintaining machine learning models in production environments.

• Proficiency with Docker and Kubernetes for containerization and scalable machine learning system design.

• Hands-on experience with cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure, including GPU orchestration.

• Experience designing, implementing, and maintaining CI/CD pipelines using tools such as GitHub Actions, Jenkins, or equivalent platforms.

• Familiarity with machine learning experiment tracking and pipeline automation tools, such as MLflow, Weights & Biases, and Kubeflow.

• Strong understanding of data versioning, model reproducibility, production monitoring, and model lifecycle management.

• Excellent analytical and problem-solving skills, with the ability to diagnose complex technical issues and develop effective solutions.

• Strong collaboration and communication skills, with the ability to work effectively across research, machine learning, and software engineering teams.

• Experience training models from scratch, including architecture selection, dataset curation and cleaning, training parameter tuning, and production monitoring, is an asset.

• Familiarity with model optimization techniques such as quantization, distillation, TensorRT, and ONNX is an advantage.

• Experience with infrastructure-as-code tools such as Terraform or CloudFormation is beneficial.

• A background in distributed systems or high-performance computing is a plus.

• Contributions to open-source projects are considered an asset.

• Willingness to undergo comprehensive background checks and participate in occasional in-person company or departmental meetings, typically once or twice per year.

Benefits:

• Competitive base salary ranging from CAD $150,000 to $200,000, depending on skills, experience, qualifications, internal pay equity, and compensation philosophy.

• Competitive compensation packages designed to recognize employees' contributions and expertise.

• Paid time off to support rest, personal commitments, and work-life balance.

• Benefits intended to support employees' health and the well-being of their families, subject to employment type and applicable eligibility conditions.

• Remote work environment offering flexibility and opportunities to collaborate with distributed teams.

• Significant technical ownership as the first MLOps Engineer, with the opportunity to establish foundational infrastructure, standards, and practices.

• Opportunities for continuous learning, professional development, and technical growth.

• A collaborative culture that encourages ownership, a growth mindset, open and respectful communication, and customer-focused decision-making.

• An inclusive workplace that welcomes candidates from diverse backgrounds and values different perspectives and experiences.

• Reasonable accommodations throughout the application, interview, and pre-employment assessment processes.

• Occasional travel for in-person company-wide or departmental meetings, generally one to two times per year.

• Please note that benefits may vary depending on employment type, including full-time, part-time, or contract arrangements.