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Senior Machine Learning Ops Engineer
| Company | Prenuvo |
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| Location | Vancouver, BC, Canada |
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| Salary | $131000 – $197000 |
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| Type | Full-Time |
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| Degrees | Bachelor’s, Master’s |
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| Experience Level | Senior |
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Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
- At least 5 years of experience in software engineering, MLOps, or ML infrastructure roles.
- Strong proficiency in Python and relevant ML engineering tooling for dependency management, packaging, testing, and deployment (e.g., Poetry, Pytest, Pylint).
- Hands-on experience with ML workflow orchestration tools (e.g., MLflow, Kubeflow, Airflow, SageMaker, Weights & Biases).
- Expertise in designing and managing CI/CD pipelines for ML applications using GitHub Actions, Jenkins, or similar tools.
- Experience with cloud-based ML infrastructure (e.g., AWS, GCP, Azure) and containerized deployments using Docker and Kubernetes.
- A strong sense of ownership, quality, and engineering best practices in ML production environments.
Responsibilities
- Design, implement, and optimize scalable MLOps infrastructure to support data ingestion, model training, evaluation, and inference at scale.
- Develop and maintain CI/CD pipelines for automating ML workflows, including training, validation, and deployment of ML models.
- Build robust containerization and orchestration strategies for ML artifacts and services using Docker and Kubernetes.
- Automate monitoring, logging, and alerting for ML models in production to ensure reliability and performance.
- Establish and enforce best practices for ML model versioning, governance, and reproducibility using tools such as MLflow or Kubeflow.
- Collaborate with data scientists, ML engineers, and DevOps teams to streamline the transition of ML models from research to production.
- Contribute to regulatory documentation and compliance processes (FDA, IDE, etc.) to support ML model deployment in regulated environments.
Preferred Qualifications
- Familiarity with deep learning frameworks like TensorFlow and PyTorch, particularly in the context of deployment and optimization.
- Experience with medical imaging applications and regulatory compliance requirements.
- Knowledge of microservices and API development frameworks such as FastAPI, REST, and gRPC.
- Understanding of distributed computing frameworks such as Ray or Spark for ML scaling.
