Devops Engineer, Machine Learning and AI

Summary

This role is part of the MLOps team in the Data Engineering and AI organization at Bell. As part of this role, you will have the opportunity to contribute to the DevOps setup, Infrastructure and Architecture of a variety of Machine Learning and AI projects at Bell, setting and following standards for the development, testing and production of AI applications on the cloud. This role is important in ensuring that Data Scientists and Machine Learning engineers are provided with all the tools and guardrails they need for seamless development of their models and AI applications in a standardized, secure manner; and helping their applications get deployed to production on the Cloud.

Key Responsibilities

• Develop, maintain and optimize CI/CD pipelines for development, testing and deployment of the various components of ML and AI applications as part of the MLOps team at Bell, taking into account testing, security, documentation, version controlling, and deployments
• Contribute to, and optimize DevOps processes for ML and AI applications 
• Define and set up development, test, release, update, and support processes for production applications on the cloud
• Deploy ML and AI applications to production while ensuring all DevOps best practices and checks have been followed
• Provision, maintain and optimize infrastructure for ML and AI applications through infrastructure-as-code
• Participate in cloud architecture and design for ML and AI applications and recommend best practices for application architectures such as micro-services, containerization etc.
• Implement application health monitoring, logging and alerting for production applications
• Contribute to MLOps and DevOps best practices and standards at Bell

Critical Qualifications

• 3+ years of hands-on DevOps experience including source code management, building and maintaining CI/CD pipelines, secrets management, infrastructure provisioning and application deployment and maintenance
• 3+ years of experience working on the Google Cloud Platform (GCP)
• Strong understanding and demonstrable knowledge of Cloud Architecture and system design on the Cloud
• Strong working knowledge of network infrastructure in GCP such as shared VPC, Cloud Load Balancing, Firewall, Private Google Access (PGA), and Private Service Access (PSA)
• Proven experience in Identity and Access Management; and secrets management through least privilege principles, preferably on GCP
• Experience building and maintaining cloud-first monitoring, logging, and alerting infrastructure
• 2+ years of experience in Python scripting for task automation
• Experience with provisioning infrastructure through Infrastructure-as-Code (preferably Terraform)
• Strong written and verbal communication skills
• Strong teamwork skills
• Ability to prioritize requests and handle multiple initiatives
   

Preferred Qualifications

•    Previous experience deploying production-level machine learning models and model training pipeline
•    Strong background in maintaining production-grade machine learning applications in GCP
•    Experience with the following GCP products: Vertex AI, Dataflow, Dataproc, Cloud Composer, BigQuery, Redis and Google Cloud Operations 
•    GCP Certifications (Associate Cloud Engineer, Professional Cloud Architect, or Professional Cloud DevOps Engineer)