DevSecOps and AI platform engineering for federal and cloud-native missions
Rackner operates at the intersection of DevSecOps, AI/ML, and mission-critical infrastructure—serving both federal agencies and hypergrowth startups. The tech stack (Kubernetes, Terraform, OpenShift, AWS EKS, Python, Go, Kafka) reveals a deep platform-engineering competency, while the hiring velocity (14 roles posted in 30 days, heavily weighted toward senior engineers) and project list (devsecops platform, mission software platform, big bang product) signal aggressive scaling in secure infrastructure deployment. Pain points around operationalizing AI/ML in compliance-heavy environments and building repeatable proposal processes suggest Rackner is moving from custom services toward productized offerings.
Notable leadership hires: Technical Lead, Business Development Lead
Rackner is a 51–200-person DevSecOps and cloud-native engineering firm based in Silver Spring, Maryland, founded in 2015. The company delivers infrastructure, platform, and AI solutions across datacenter, public cloud, private cloud, and edge environments for federal agencies (civilian and defense) and startup customers. Core capabilities include Kubernetes-based deployment architectures, hybrid-cloud application development, microservices, and mission-critical AI/ML operationalization. Rackner is a Kubernetes Certified Service Provider and CNCF member, with established partnerships across AWS, Azure, and GCP.
Rackner's primary stack includes Kubernetes, Terraform, Docker, AWS EKS, OpenShift, Python, Java, Go, React, Angular, and Kafka. They also use Helm, RKE2, VMware Tanzu, FastAPI, Django, Spring Boot, and GitLab for CI/CD.
Active projects include a devsecops platform for mission application owners, unified kubernetes-based platform services, mission software platform development, and AI/ML operationalization in secure environments. Rackner is also building repeatable proposal and project delivery processes.
Rackner's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.