We are seeking a highly skilled and motivated Senior Platform Engineer with a strong focus on MLOps to join our dynamic team in Toronto, Ontario, Canada. This is a full-time, hybrid role offering a competitive salary range of USD 95,000 – 125,000 per year. In this pivotal role, you will be instrumental in designing, building, and maintaining robust, scalable, and secure machine learning platforms that empower data scientists and ML engineers to rapidly develop, deploy, and monitor ML models in production environments. You will drive the adoption of best practices for MLOps, automation, and cloud infrastructure, ensuring operational excellence and continuous innovation.
Key Responsibilities:
- Design, implement, and manage end-to-end MLOps pipelines using tools like Kubeflow, MLflow, and Apache Airflow to automate model training, versioning, deployment, and monitoring.
- Develop and maintain cloud-native infrastructure on AWS (or similar cloud providers like GCP) using Infrastructure as Code (IaC) principles with Terraform.
- Build and optimize CI/CD pipelines specifically tailored for machine learning workloads, ensuring reliable and repeatable deployments.
- Implement robust monitoring, logging, and alerting solutions for ML models and infrastructure using tools such as Prometheus, Grafana, and cloud-native services.
- Collaborate closely with Data Scientists, ML Engineers, and Software Engineers to understand their needs and provide expert guidance on platform utilization and best practices.
- Ensure the security, compliance, and cost-effectiveness of our ML platform and underlying infrastructure.
- Drive continuous improvement of the platform through evaluation of new technologies and methodologies.
Required Skills & Tools:
- 5+ years of experience in Platform Engineering, DevOps, or SRE roles, with a strong emphasis on MLOps.
- Proficiency in at least one programming language (e.g., Python, Go) for automation and platform development.
- Extensive experience with containerization technologies, especially Kubernetes, and orchestrating complex workloads.
- Demonstrable expertise with a major cloud platform (AWS preferred, or GCP) for infrastructure provisioning and management.
- Hands-on experience with Infrastructure as Code (IaC) tools like Terraform.
- Solid understanding and practical experience with MLOps frameworks and tools such as Kubeflow, MLflow, and Apache Airflow.
- Strong background in CI/CD practices and tools (e.g., GitLab CI, Jenkins, ArgoCD).
- Experience with monitoring and logging solutions (e.g., Prometheus, Grafana, ELK Stack).
Nice-to-Have:
- Experience with data warehousing and big data technologies (e.g., Apache Spark, Snowflake, Databricks).
- Familiarity with various machine learning frameworks (e.g., TensorFlow, PyTorch).
- Prior experience working in a regulated industry or with stringent compliance requirements.
- Certifications in cloud platforms (e.g., AWS Certified DevOps Engineer) or Kubernetes (CKA, CKAD).
What We Offer:
Join a forward-thinking team where innovation is at the core of everything we do. We offer a challenging yet rewarding environment with opportunities for continuous learning and professional growth. Our comprehensive benefits package includes health, dental, and vision coverage, generous paid time off, and a flexible hybrid work model that supports work-life balance. You'll work on impactful projects that leverage cutting-edge technology to solve real-world problems, contributing directly to the success of our products and services.
