Senior MLOps Engineer

Senior MLOps Engineer Location: Canada Work setup: Remote / Hybrid Employment type: Full-time ABOUT CORSphere CORSphere builds AI systems for mission-critical machinery and operational environments across marine, industrial, and defence-adjacent sectors. Our platform helps organizations detect issues earlier, reduce non-actionable alerts, and make faster, more informed maintenance and operational decisions. We are hiring a Senior MLOps Engineer to help scale the infrastructure, deployment systems, and model lifecycle workflows behind that platform. THE ROLE This is a senior, hands-on engineering role for someone who knows how to move machine learning systems from experimentation into reliable production use. You will work across infrastructure, deployment, observability, and model lifecycle management to help ensure our AI systems perform in real operational settings, not just in development environments. You will collaborate closely with ML engineers, software engineers, and product leadership to productionize core AI capabilities and support deployments across cloud, on-premise, hybrid, and other constrained environments. WHAT YOU WILL DO Design, build, and maintain MLOps infrastructure that supports model development, evaluation, deployment, and monitoring. Productionize ML workflows for anomaly detection, predictive maintenance, ranking, and related AI capabilities. Build reproducible pipelines for data ingestion, preprocessing, training, validation, packaging, and release management. Improve model and dataset traceability through versioning, experiment tracking, artifact management, and deployment controls. Develop and maintain CI/CD workflows for machine learning systems and related platform components. Optimize inference and deployment workflows for latency, reliability, memory efficiency, and operational robustness. Support deployments across local, hybrid, cloud, and security-sensitive environments. Help define observability, alerting, monitoring, and rollback standards for production ML systems. Collaborate across engineering and product to translate operational requirements into scalable technical systems. Improve internal tooling and automation to reduce deployment friction and increase engineering velocity. WHAT WE ARE LOOKING FOR 4+ years of experience in MLOps, ML infrastructure, DevOps, platform engineering, or a closely related role. Strong Python experience and solid software engineering fundamentals. Hands-on experience with Docker and Kubernetes. Experience building and maintaining CI/CD systems using GitHub Actions or similar tooling. Experience with model lifecycle and orchestration tools such as MLflow, Airflow, Kubeflow, DVC, LakeFS, Weights & Biases, or comparable platforms. Strong understanding of deploying and maintaining machine learning systems in production. Familiarity with Linux environments and comfort working from the command line. Experience with monitoring, observability, and production reliability practices. Strong communication, ownership, and problem-solving skills in cross-functional environments. NICE TO HAVE Experience working with time-series, industrial, sensor, or operational datasets. Experience with predictive maintenance, anomaly detection, ranking systems, or related applied ML use cases. Familiarity with PyTorch and ML model serving workflows. Experience deploying systems in on-premise, edge, air-gapped, or other constrained environments. Exposure to secure ML infrastructure and production controls for sensitive environments. Prior experience in marine, industrial, energy, aerospace, or defence-adjacent domains. Experience supporting implementation or customer-facing technical deployments. Familiarity with LLM-enabled workflows or operator-facing AI systems. WHY JOIN CORSPHERE Work on AI systems designed for real-world, mission-critical environments. Help shape the infrastructure foundation behind a growing applied AI platform. Take on meaningful ownership across architecture, deployment, and production reliability. Join a team building practical, high-impact technology for complex operational settings. HOW TO APPLY Please submit your resume along with any relevant GitHub, portfolio, or project links that demonstrate your experience building and shipping production-grade ML systems. We know strong candidates do not always match every line of a job description exactly. If this role is aligned with your experience and interests, we encourage you to apply. CORSphere is committed to building an inclusive and respectful workplace and welcomes applications from candidates of diverse backgrounds. Accommodations are available throughout the hiring process for candidates who require them.

Design and maintain MLOps infrastructure to support the development, deployment, and monitoring of AI systems for mission-critical machinery. Build reproducible pipelines and CI/CD workflows to productionize ML capabilities across cloud, on-premise, and hybrid environments.

Requires 4+ years of experience in MLOps or platform engineering with strong Python and software engineering fundamentals. Proficiency with Docker, Kubernetes, and model orchestration tools like MLflow or Airflow is essential.

Relevées dans la description du poste. Confirmez les exigences importantes ci-dessus.

• MLOps
• Python
• Docker
• Kubernetes
• CI/CD
• GitHub Actions
• MLflow
• Airflow
• Kubeflow
• DVC
• LakeFS
• Weights & Biases
• Linux
• Model Monitoring
• PyTorch
• Model Serving

• Technology
• Software
• Engineering
• Data & Analytics
• Manufacturing

Expérience minimale

5+ ans

Postuler avant le

29 août 2026

Langue de l’offre

anglais

Heures de travail

40 heures par semaine

Niveau d’expérience

Mid-Senior level

Mode de candidature

La candidature directe est offerte