Our client is a leading Private Equity firm in Canada. They are looking for a Senior MLOps/DevOps Engineer.
Role-Specific Accountabilities:
Cloud Solutions:
• Design, deploy, and maintain scalable solutions across major cloud platforms, with a preference for Google Cloud (GCP), AWS, or Azure.
• Ensure availability, performance, and readiness of Core infrastructure, applications, and services.
Microservices and ML Pipelines:
• Implement and maintain end-to-end microservices and machine learning pipelines.
• Manage the entire ML lifecycle from data ingestion to monitoring in production.
CI/CD Implementation:
• Utilize CI/CD principles to streamline code deployments and software updates.
• Automate the deployment of ML algorithms into production using Infrastructure as Code tools.
Cross-Functional Collaboration:
• Collaborate with software developers, DL/ML engineers, and other teams for efficient delivery.
• Enhance the overall software delivery pipeline and ensure infrastructure health.
Monitoring and Security:
• Implement and manage monitoring tools for system health and performance.
• Enforce security best practices and vulnerability management standards.
• Ensure compliance with regulations and company policies for ML models and data pipelines.
Continuous Improvement:
• Stay updated with emerging trends and tools in DevOps and MLOps.
• Proactively seek and recommend opportunities for improvement.
Education, Experience & Capabilities:
• Bachelor's or higher degree in Computer Science, Engineering, or a related field.
• 7 years of experience in DevOps roles, with at least 1 year specifically in MLOps or handling ML in production.
• Mastery in scripting languages like Python, Shell, or equivalent.
• Deep expertise in major cloud platforms, especially Google Cloud Platform (GCP).
• Hands-on experience with containerization technologies (Docker) and orchestration tools (Kubernetes).
• Proven experience with Infrastructure as Code tools (Cloud Deployment Manager, CloudFormation, or Terraform).
• Experience with version control systems (git) and collaboration platforms (GitHub or GitLab).
• Familiarity with MLOps tools like TensorFlow, TFX, MLflow, or KubeFlow (GCP).
• Experience in deploying ML models into production and understanding model architectures.
• Documentation and Security:
• Creating comprehensive documentation for ML and infrastructure workflows.
• Understanding of network architectures, VPC designs, and security best practices in cloud environments.
• Relevant certifications such as Google Cloud Professional DevOps Engineer are a plus
