Federal AI modernization and cloud migration for agencies
LightFeather modernizes legacy federal systems into AI-driven, cloud-native platforms for U.S. government agencies. The stack is Azure-heavy (with GCP secondary), Docker/Kubernetes-based, and infrastructure-as-code first—matching their stated focus on DevSecOps and compliance automation. Active hiring is almost entirely engineering-focused (25 of 26 roles), weighted toward senior and lead levels, signaling depth-over-breadth scaling in a compliance-constrained market where skilled practitioners are scarce.
LightFeather is a Veteran-Owned Small Business (VOSB), Service-Disabled Veteran-Owned Small Business (SDVOSB), and Woman-Owned Small Business (WOSB) founded in 2018 and based in Arlington, Virginia. The company designs, builds, and operates AI/ML systems, modern data platforms, and cloud applications for federal agencies including DOJ, DHS, USCIS, IRS, and the State Department. Their work spans ML models trained on hundreds of millions of federal records, real-time analytics on Databricks, and full-lifecycle deployments in classified environments. Projects include ServiceNow implementations, cloud infrastructure modernization, Terraform IaC development, and identity/biometric systems for immigration and law enforcement.
LightFeather primarily uses Azure (Virtual Desktop, AKS, Functions, Key Vault, AD, Monitor) with GCP as secondary (BigQuery, Cloud Functions, VPC). Both support multi-cloud federal deployments.
Core: Azure, GCP, Terraform, Kubernetes, Docker, GitLab CI/CD, Jenkins. Languages: Python, Go, Bash, PowerShell. Data: BigQuery, Databricks. Security/ops: Azure AD, Key Vault, FSLogix, Ansible.
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LightFeather'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.