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Build and deploy cloud-native applications for Department of Defense customers. The role focuses on Cloud/DevOps engineering for a large data-processing application, using AWS, GitLab CI/CD, Kubernetes, and source control. Responsibilities include supporting software development lifecycle practices, collaborating cross-functionally, continuously improving team processes, and potentially deploying observability tooling. Preferred experience includes Python, DoD customers, OpenShift, Istio, and Argo CD.

Develop and optimize computer vision and image-processing algorithms for remote sensing imagery, including object detection, segmentation, tracking, registration, and feature extraction. Build high-performance software in Python and C++, accelerate algorithms with NVIDIA CUDA and GPUs, and translate research into reliable, maintainable applications. Work primarily at Wright-Patterson Air Force Base with a small team supporting defense customers.

Administer Linux servers and supporting infrastructure, monitor application and database health, troubleshoot production issues, manage vulnerabilities and backups, validate recovery procedures, and support reliable mission systems. The role includes shift coverage, onsite weekend health checks, incident communication, documentation, teammate training, and modernization of established systems. An active TS/SCI clearance and extensive systems administration experience are required.

Manage full-cycle recruiting for technical roles, including sourcing, screening, interview coordination, offer negotiation, and hiring support. Partner with hiring managers and leadership to define requirements, build talent pipelines, track recruiting activity in Greenhouse, and improve hiring processes. Recruit across software engineering, cloud, DevOps, AI/ML, research, defense, and intelligence roles while maintaining strong candidate relationships and representing the company at recruiting events.

Build and deploy cloud-native applications and support a large data-processing application. Develop and maintain cloud infrastructure using AWS, GitLab CI/CD, Kubernetes, and source control. Collaborate within an Agile technical team, improve development processes, and communicate with technical and non-technical stakeholders. Preferred experience includes Python, Department of Defense environments, Kubernetes observability tools, OpenShift, Istio, and ArgoCD.

Support and improve enterprise multitenant PaaS environments across Kubernetes clusters. Responsibilities include Tier 3 tenant support, platform operations, cloud deployments, infrastructure automation, GitOps and CI/CD pipelines, observability, security compliance, and scalable platform design. The role requires active Top Secret clearance, U.S. citizenship, onsite work in St. Louis, and experience with Kubernetes, cloud platforms, infrastructure as code, scripting, and automated operational workflows.

Develop and optimize physics modeling and simulation algorithms using Python to analyze sensor data. Apply expertise in aerodynamics and aerothermal simulations across supersonic and hypersonic vehicle regimes, while following software engineering and configuration-management practices. Support defense customers at Wright-Patterson Air Force Base and collaborate with a small engineering team. Experience with modular software, containerization, databases, Git workflows, and algorithm deployment is valued.

Supports cloud and on-premises infrastructure, user provisioning, help desk tasks, scripting, automation, observability, documentation, vulnerability scanning, patch management, and incident response. The intern works with AWS, GCP, Azure, Linux, monitoring tools, and infrastructure-as-code technologies under the mentorship of senior engineers and DevOps leads.

Develop automation, improve cluster reliability, and enhance workload deployment in multi-tenant Kubernetes environments. Maintain platform components, contribute to tooling, and assist with monitoring, troubleshooting, and cluster operations. Gain hands-on experience across the product engineering lifecycle, including requirements definition, implementation, infrastructure-as-code, GitOps, observability, container orchestration, and cloud infrastructure.

The Senior AI/ML Engineer will develop and enhance AI/ML models using Python, specializing in OPIR domain expertise, with a focus on object identification and classification.