Mission-critical software and systems engineering for government and defense
Praxis Engineering is a GDIT subsidiary building infrastructure, security, and analytics platforms for defense and government clients. The tech stack—Red Hat, OpenShift, Elasticsearch/Logstash/Kibana, Ansible, Docker—reflects heavy infrastructure and observability work; hiring is heavily skewed toward senior and principal engineers (31 of 34 roles), suggesting deep domain complexity and customer relationships that demand experience over scale.
Praxis Engineering, headquartered in Annapolis Junction, Maryland, is a public company (GDIT subsidiary) focused on applied software and systems engineering for government and mission-critical environments. The company operates across infrastructure automation, high-performance computing, cybersecurity, and streaming analytics—domains reflected in both stack and project portfolio. Active projects include HPC infrastructure maintenance, Linux cluster provisioning automation, privileged access management tooling, and Kubernetes cluster operations. The 201–500 employee organization is entirely US-based and currently accelerating hiring, with 27 roles posted in the last 30 days.
Primary stack includes Red Hat Enterprise Linux, CentOS, Ubuntu, OpenShift, Docker, Podman, Ansible, Salt, and Java. Observability and analytics layer: Elasticsearch, Logstash, Kibana, Zabbix. Data and messaging: MySQL, PostgreSQL, MongoDB, RabbitMQ. Tooling: Jira, Confluence, GitLab CI/CD. Currently adopting Rust.
Annapolis Junction, Maryland. The company is a subsidiary of General Dynamics Information Technology (GDIT) and is public. All hiring is currently confined to the United States.
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Praxis Engineering'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.