DevOps Engineer

## About Later 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. ## About this position 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. ## What you'll be doing ### Strategy - 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. ### Technical/ Execution - 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. ### Team / Collaboration - 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. ### Research/Best Practices - 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. ## What success looks like First 30 Days — Learn, Support, and Understand 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. They will begin supporting existing deployment pipelines, observability tools, infrastructure documentation, and operational workflows. They will also start collaborating with cross-functional teams to understand the needs and challenges of the data and product teams.