Our client, a fast growing PE firm in downtown Toronto is looking for a DevOps Engineer with MLOps Focus 🌐🚀
• ** HYBRID Downtown Toronto- local candidates only ***
Role-Specific Accountabilities:
• 🌐 Scalable Cloud Solutions: Design, deploy, and maintain scalable solutions across major cloud platforms, preferably Google Cloud, ensuring the 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, facilitating a smooth transition from data ingestion, training, testing, deployment to monitoring in production.
• 🚀 CI/CD Excellence: Leverage CI/CD principles to streamline code deployments and software updates, ensuring a seamless transition from development to production environments. Automate the deployment of ML algorithms using Infrastructure as Code tools like Google Cloud Development Manager or CloudFormation.
• 👥 Cross-Functional Collaboration: Work cross-functionally with software developers, DL/ML engineers, and other team members to ensure efficient delivery, enhance the overall software delivery pipeline, and maintain high-quality machine learning models.
• 📊 Monitoring and Reliability: Implement and manage monitoring tools to ensure system health, diagnose potential issues, and provide feedback loops for development teams. Monitor the health and performance of production infrastructure and ML models, implementing real-time monitoring, logging, and alerts for high uptime and reliability.
• 🔒 Security Best Practices: Enforce security best practices and vulnerability management standards across the entire development lifecycle and infrastructure. Ensure ML models and data pipelines comply with relevant regulations and company policies.
• 🌐 Continuous Learning: Stay updated with emerging trends and tools in DevOps and MLOps, continuously seeking opportunities for improvement and proactively recommending them.
Education, Experience & Capabilities:
• 🎓 Educational Background: Bachelor's or higher in Computer Science, Engineering, or a related field.
• 🌐 Professional Experience: 7 years of experience working in DevOps roles, with a minimum of 1 year specifically in MLOps or handling ML in production.
• 🚀 Scripting Mastery: Mastery in scripting languages like Python, Shell, or equivalent.
• ☁️ Cloud Expertise: Deep expertise in major cloud platforms is a must, especially Google Cloud Platform (GCP).
• 🐳 Containerization and Orchestration: Hands-on experience with containerization technologies like Docker and orchestration tools such as Kubernetes.
• 🛠️ Infrastructure as Code: Proven track record with Infrastructure as Code tools like Cloud Deployment Manager, CloudFormation, or Terraform.
• 🔄 Version Control and Collaboration: Proven experience with version control systems, primarily git, and collaboration platforms like GitHub or GitLab. Understanding of the code development lifecycle is a must.
• 🔧 MLOps Familiarity: Familiarity with MLOps tools like TensorFlow, Extended (TFX), MLflow, or KubeFlow (GCP). Experience in deploying ML models into production and understanding different model architectures and their infrastructure requirements.
• 📜 Documentation Skills: Creating comprehensive and clear documentation for ML and infrastructure workflows, tools, and systems. Comprehensive understanding of network architectures, VPC designs and setup, and security best practices in cloud environments is a PLUS. Experience in implementing identity and access management and other security protocols relevant to data and ML workflows
