Associate, DevOps Engineer

BMO•Toronto, ON

2d•CA$90,000 - CA$120,000

About The Position

We are seeking an experienced DevOps Engineer to join our Data Cognition Team at BMO Capital Markets. In this role, you will design, build, and maintain robust DevOps pipelines and infrastructure to support AI and data-driven applications across Investment Banking and Global Markets. You will play a critical role in enabling scalable, secure, and automated environments for deploying advanced analytics and machine learning solutions. The Data Cognition Team (DCT) at BMO Capital Markets delivers a scalable, customizable, and sustainable suite of AI-enabled products for multiple business units. We leverage cutting-edge technologies to solve complex business challenges and drive strategic transformation across Investment Banking, Global Markets, and other divisions.

Requirements

University degree in Computer Science, Engineering, Mathematics, or related technical field.
Strong experience with cloud platforms (AWS, Azure, GCP).
Hands-on experience with k8s and ArgoCD and deployment of various range of services such as Kafka, Druid, and OpenTelemetry.
Proficiency in programming languages such as Python, Go, and Bash.
Experience with Infrastructure as Code tools (Terraform, Helm, Kustomize).
Knowledge of cloud security frameworks and tools (CSPM, DSPM, ASPM).
Experience with containerized environments and OCI image building.
Experience with DevOps tools (GitHub, GitLab CI, Jenkins, Prometheus, Grafana, Elasticsearch, Kibana).
Strong understanding of security protocols (Kerberos, TLS, OAuth2).
Excellent communication skills for collaboration with technical and business stakeholders.

Nice To Haves

Certifications such as CKA (Certified Kubernetes Administrator), CKAD (Certified Kubernetes Application Developer), or AWS/Azure/GCP cloud certifications.
Experience with Generative AI, Agentic AI, or similar advanced AI technologies, including building and deploying intelligent agents and generative models.

Responsibilities

Develop and maintain end-to-end DevOps pipelines for AI and data applications, ensuring reliability and scalability.
Enhance build infrastructure for in-house data and analytics applications.
Implement Infrastructure as Code (IaC) for platform components and create automation tools for efficient platform management.
Maintenance and upgrading of server infrastructure including multiple clusters.
Improve monitoring facilities across diverse applications and troubleshoot infrastructure issues promptly.
Integrate enterprise technologies for application release and system integration.
Stay current with emerging DevOps, AI/ML, and cloud technologies to continuously improve processes.