Public Trust Eligibility Required This is a contingent position, meaning employment is dependent upon the successful award of the associated contract to Aretum and completion of any required background investigation or security clearance verification.  About Aretum  Aretum is a mission-driven organization committed to delivering innovative, technology-enabled solutions to our customers across defense, civilian, and homeland security sectors. Our teams work at the intersection of strategy, technology, and transformation, helping agencies solve their most critical challenges. We believe in investing in our people and creating a culture where collaboration, inclusion, and professional growth are at the forefront.  Job Summary Aretum is seeking a skilled and motivated Senior DevOps Engineer. As a Senior DevOps Engineer, you will manage a DevOps team that is implementing and executing processes to create and fully maintain automated CI/CD pipelines for application deployments in an AWS environment. Due to the nature of our work as a federal consulting organization, employees may be expected to handle Controlled Unclassified Information (CUI) and must adhere to applicable safeguarding and compliance requirements.   Responsibilities Perform the following functions in an AWS environment: - Continuous Integration and Continuous Delivery (CI/CD) Pipeline Development and Maintenance - Monitoring and Logging - Security and Compliance - Performance Optimization and Scaling - Documentation and Knowledge Transfer - Infrastructure Setup and Configuration - Business Continuity and Disaster Recovery (BCDR) - Remediate Security Vulnerabilities - Incident Response / Production Support - Troubleshooting

• Responsible for establishing and/or technically updating continuous integration, continuous delivery, and software infrastructure automation policies; • Defining standards and best practices for system development and deployment; • Ensuring efficiency, security, and quality; • Collaborating with other teams to implement DevOps solutions; • Identifying opportunities to improve and optimize processes; • Providing technical support on DevOps-related issues; • Performing other related duties inherent to the role.

• Contribute to the design, development, and delivery of features that enhance system reliability and scalability. • Define, measure, and improve SLIs, SLOs, and error budgets in collaboration with engineering teams. • Participate in building a culture of reliability through knowledge sharing, documentation, and process improvements. • Implement and improve observability tooling and practices to monitor the health and performance of production systems. • Participate in incident management, including on-call rotations, root cause analysis, and postmortem reviews. • Lead smaller initiatives or components of larger projects, ensuring technical quality and operational readiness. • Collaborate with software engineering, security, and product teams to ensure resilient and secure system design. • Mentor junior engineers, sharing expertise in SRE principles and AWS best practices. • Contribute to automation efforts to reduce toil and improve efficiency of operational processes.

• Lead operations for multi-tenant SaaS workloads on AWS, ensuring scalability, high availability, and cost efficiency • Design, implement, and maintain reliable infrastructure for production, data, and AI/ML workloads • Own incident response, postmortems, and operational runbooks to improve system reliability and reduce MTTR • Manage and enhance CI/CD pipelines supporting both application and ML deployment workflows • Build and maintain infrastructure automation using Infrastructure as Code (AWS CDK or Terraform) • Enable self-service capabilities for engineering and data science teams • Monitor and optimize cloud usage across compute, GPU, and storage resources, implementing cost controls and forecasting • Support and automate ML pipelines, including training, testing, and deployment using AWS SageMaker, Kubeflow, or MLflow • Manage GPU and compute clusters (EKS, ECS, EC2) for model training and inference workloads • Develop and maintain monitoring, alerting, observability, and security best practices • Collaborate closely with Engineering, Data, AI/ML, and PlatformOps teams to ensure smooth cross-team delivery