DevOps Engineer

Later • Vancouver, BC

19d • CA$120,000 - CA$140,000 • Hybrid

About The Position

Later is the world’s most intelligent influencer marketing company, built to give brands the confidence to create unforgettable campaigns. By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing’s most visible investments.
Built on a native, AI-powered platform and more than a decade of proprietary data—including billions of social interactions, impressions, and $2.4B+ in verified influencer-driven purchases—Later helps teams understand what will work before they launch.
By combining trusted insight with expert guidance, Later removes guesswork from influencer marketing, enabling brands to choose the right creators, execute fully managed campaigns, and drive meaningful growth across awareness, engagement, and revenue. Trusted by leading enterprise brands including Nike, Wayfair, Unilever, and Southwest Airlines, Later bridges creativity and performance so campaigns don’t just look good—they deliver results. Learn more at later.com.
We’re looking for a DevOps Engineer to help support and grow Later’s cloud infrastructure, DevOps practices, and emerging MLOps capabilities.
This role reports to the Infrastructure team and is primarily focused on DevOps, AWS, Kubernetes, CI/CD, Terraform, while also helping support the infrastructure needs of our Data teams. You’ll work closely with senior engineers, platform teams, data scientists, and product engineers to help build the systems that support application delivery, data workflows, model experimentation, and ML deployment.
This is a great role for someone who has a solid DevOps foundation and wants to grow deeper into cloud infrastructure, Kubernetes, GitOps, and MLOps. You’ll help maintain reliable infrastructure, improve deployment workflows, automate repeatable tasks, and support the systems that allow engineering and data teams to move faster and more safely.

Requirements

• 2–5 years of hands-on DevOps or cloud engineering experience in production environments.
• Expertise with Kubernetes (EKS), Helm, and microservices.
• 1-2 years AWS and SageMaker Experience.
• 1-2 years creating data pipelines
• 1-2 years with LLM deployments on Kubernetes
• 1-2 years with kubernetes clusters using nodes that have GPU
• Proven track record using Terraform for scalable, auditable Infrastructure as Code.
• A collaborative, solution-oriented mindset and a passion for automation and continuous improvement.

Responsibilities

• Support the development and execution of the infrastructure roadmap across DevOps, and MLOps, aligned with product, and data/AI growth plans.
• Partner with engineering, data, and ML teams to ensure scalability, reliability, security, and automation are built into both application infrastructure and machine learning workflows.
• Help evaluate and adopt DevOps and MLOps tools that improve system efficiency, observability, developer experience, model deployment, and operational reliability.
• Contribute to platform standards that make infrastructure, CI/CD, data pipelines, and ML systems more repeatable, secure, and easier to operate.
• Support the evolution of cloud-native practices that enable faster product delivery while also preparing the platform for future AI and ML initiatives.
• Build and manage infrastructure to deploy ML models into production reliably using CI/CD pipelines , Flask-based APIs, and orchestration tools (e.g., Airflow, Kubeflow, or Argo Workflows).
• Automate training pipelines, model registry, validation, deployment, and rollback strategies using tools such as Amazon SageMaker Interface and Postman for testing.
• Build systems to monitor model performance, latency, data drift, and resource usage using Amazon CloudWatch, Prometheus, and Grafana.
• Design and maintain tools and systems to support model versioning, experiment tracking (e.g., MLflow, Amazon SageMaker Studio Notebooks), and reproducible training workflows.
• Operate across GCP and AWS to manage training/inference infrastructure, BigQuery datasets, and GPU workloads.
• Use tools like Terraform or CloudFormation to manage cloud infrastructure in a scalable, repeatable manner.
• Work with Data Scientists, Analysts, Platform Engineers, and Product Engineers to support their end-to-end ML workflows.
• Partner with Product and Data teams to streamline CI/CD workflows, GitOps practices, and deployment processes that support both application delivery and data/ML workflows.
• Work closely with Data teams to support reliable infrastructure for data pipelines, model experimentation, training workflows, and production ML deployments.
• Collaborate with Product teams to understand platform needs and ensure infrastructure decisions support product reliability, scalability, and delivery speed.
• Share documentation, runbooks, and best practices that help Product and Data teams deploy, monitor, and troubleshoot systems with more autonomy.
• Support a collaborative DevOps and MLOps culture by helping teams adopt automation, observability, and repeatable deployment patterns.
• Stay current with cloud-native, DevOps, data infrastructure, and MLOps trends, identifying tools, patterns, and practices that can improve reliability, automation, scalability, and delivery speed.
• Continuously evaluate infrastructure, CI/CD pipelines, data workflows, model deployment processes, performance, cost, and efficiency, recommending improvements that align with product, engineering, and data team goals.
• Contribute to documentation, runbooks, incident reviews, and post-mortem processes to strengthen operational learning across both DevOps and MLOps practices.
• Help define and share best practices for infrastructure-as-code, GitOps, observability, secure deployments, data pipeline reliability, and ML workflow automation.
• Look for opportunities to simplify systems, reduce manual work, and improve the developer and data team experience through better tooling, automation, and platform standards.

Benefits

• We take a market-based & data-driven approach to compensation.
• We leverage data from trusted third-party compensation sources to help us understand the market value of a role based on function, level, geographic location, and scope.
• We evaluate compensation bi-annually, including performance and market-related factors.
• Our salaries are benchmarked against market Total Cash Compensation for the geographic location of our job posting.
• Compensation for some roles is structured as On Target Earnings (OTE = base + commission/variable) while for others it is structured as Salary only.
• To comply with local legislation and ensure transparency, we share salary ranges on all job postings.
• Skills, experience and other factors help determine the final salary we offer which may vary from the original range posted.
• Additionally, all permanent team members are eligible to participate in various benefits plans as part of their overall compensation package.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed