Senior DevOps/MLOps Engineer for Kubernetes & CI/CD

Global Relay; United States; Sur place; USD 63 000 - 91 000; Plein temps

Avantages offerts par ce poste

Extended health benefits

Wellness allowance

Vacation days

Bonus

RRSP matching

Meal program Vancouver

Résumé du poste

Global Relay is seeking a Senior DevOps/MLOps engineer to ensure reliable operation of our AI-driven platforms. You will build automation for CI/CD, deploy services to Kubernetes, and monitor systems across multiple environments.

The role emphasizes cross-team collaboration, scaling infrastructure, and supporting machine learning model delivery from build to production. You will work with a variety of tools—Jenkins, Docker, Kubernetes, and cloud-native stacks—while mentoring junior teammates and

Qualifications

• 5+ years of experience in a DevOps/MLOps role.
• Bachelor's degree in computer science or related field, or equivalent.
• Strong understanding of CS fundamentals such as threading, OOP, and more.
• Knowledge of networking, firewalls, protocols, databases and related concepts.
• Experience mentoring junior and intermediate DevOps/MLOps professionals.

Responsabilités

• Automation: build CI/CD tooling to improve delivery automation.
• Deployments: roll out services to Kubernetes using CI/CD pipelines.
• Operations: monitor and ensure reliability across environments.
• Service reliability: provide troubleshooting and dashboards/log reviews to address issues.

Connaissances

DevOps experience

Mentoring

Communication

Networking basics

OOP fundamentals

Formation

Bachelor's degree in CS

Outils

Jenkins

Docker

Kubernetes

Python

CockroachDB

PostgreSQL

Kafka

Splunk

Prometheus

Artifactory

npm

Description du poste

Global Relay is seeking a Senior DevOps/MLOps engineer to ensure reliable operation of our AI-driven platforms. You will build automation for CI/CD, deploy services to Kubernetes, and monitor systems across multiple environments.

The role emphasizes cross-team collaboration, scaling infrastructure, and supporting machine learning model delivery from build to production. You will work with a variety of tools—Jenkins, Docker, Kubernetes, and cloud-native stacks—while mentoring junior teammates and