Later
DevOps Engineer
Vancouver, British Columbia, Canada · Senior
Sponsorship not specifiedDetected 62 days ago
Node.jsFlaskBigQueryAWSGCPCloud PlatformsKubernetesTerraformHelmCI/CDPrometheusDevOpsMachine LearningAirflowData EngineeringLLMsMLOpsA/B TestingInfluencer MarketingPostmanResearchCollaboration
About the role
- By combining real creator relationships, trusted intelligence, and expert guidance, Later removes fear and guesswork from one of marketing's most visible investments.
- 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.
- They will also help improve documentation, runbooks, alerting, and troubleshooting processes.
Responsibilities
- Support the development and execution of the infrastructure roadmap across DevOps, and MLOps, aligned with product, and data/AI growth plans.
- 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).
- 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.
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.
- How you work:
- Driven by Impact: You deliver results that matter-prioritizing high-value work, meeting deadlines, and adapting quickly while keeping outcomes clear.
- Strategic & Customer-Centric: You anticipate risks and opportunities, connect decisions to long-term growth, and build trust through proactive insights.
- Curious & Growth-Oriented: You seek knowledge, ask sharp questions, and apply learnings fast-challenging the status quo with a mindset of improvement.
- Collaborative & Resilient: You thrive in change by staying resourceful, solution-focused, and positive-removing roadblocks, sharing insights, and keeping morale high.
- Accountable & Honest: You own your work, hold yourself and others to a high bar, and use transparent feedback to drive growth.
Compensation
- 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.
- 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.
Benefits
- Partner with engineering, data, and ML teams to ensure scalability, reliability, security, and automation are built into both application infrastructure and machine learning workflows.
- Contribute to documentation, runbooks, incident reviews, and post-mortem processes to strengthen operational learning across both DevOps and MLOps practices.
- In the first 30 days, the candidate will focus on learning Later's cloud infrastructure, Kubernetes environments, AWS services, CI/CD workflows, and data/ML platform components.
This listing is sourced directly from Later's careers page and normalized into a canonical job model.