AI Architect; AI Security + Federal

Job Details

Salary: $200,000 - $275,000 per year

This Jobot Job is hosted by:
Craig Rosecrans

We are partnering with a rapidly growing, venture-backed technology company operating at the intersection of Artificial Intelligence and cybersecurity to hire an AI Solutions Architect focused on Federal customers.

This is not a traditional Solutions Architect position where you simply deliver presentations and hand opportunities back to Engineering.

We are looking for someone who can architect, build, deploy, demonstrate, and troubleshoot real AI solutions while working directly with mission owners across the Intelligence Community, Department of Defense, DHS, and civilian federal agencies.

You will serve as the technical lead throughout the customer journey—from initial discovery and architecture discussions through proof-of-value, deployment, integration, and technical win.

The right person will be equally comfortable writing Python, deploying Kubernetes into an air-gapped environment, building an AI proof of concept, whiteboarding an architecture, and presenting the solution to senior government stakeholders.

You’ll join a well-funded, rapidly scaling company building technology in one of the most important emerging areas of cybersecurity: protecting AI systems themselves.

The company has recently completed a significant new funding round and is continuing to expand its engineering, product, and federal organizations.

• Fully remote work within the United States
• Significant exposure to cutting-edge AI and cybersecurity technology
• Direct involvement with important federal AI initiatives
• High technical autonomy and ownership
• Opportunity to work closely with Product, Research, Engineering, and customers
• Flexible paid time off
• Company holidays
• Home-office and connectivity support
• Dedicated learning and development funding for training, certifications, conferences, and industry events

If you're a deeply technical Solutions Architect who understands Federal missions, AI/ML, cloud-native infrastructure, and security—and you want to help solve the emerging challenge of securing AI systems—we'd love to speak with you.

• Lead Federal Technical Engagements:
  Partner with federal Account Directors to lead technical strategy across complex government opportunities. Conduct discovery with mission owners, AI/ML engineers, security teams, architects, and executive stakeholders. Understand customers' AI initiatives, architectures, data pipelines, infrastructure, security requirements, and compliance constraints. Translate loosely defined mission challenges into clearly scoped technical evaluations and proof-of-value engagements. Establish measurable success criteria, timelines, technical requirements, and exit conditions.
  
  Serve as a trusted technical advisor and provide candid guidance around where the platform is—and is not—the right solution.
• Architect & Deploy Real Solutions:
  Architect and deploy an enterprise AI security platform across both connected SaaS and fully disconnected/air-gapped environments. Package and transfer container images, Helm charts, dependencies, and supporting artifacts into environments with limited or no internet connectivity. Deploy and administer Kubernetes-based environments utilizing technologies such as Kubernetes, Docker, Helm, Private container registries, Open Shift, VMware / Proxmox, AWS, including government and restricted environments.
  
  Troubleshoot deployment, networking, integration, and infrastructure issues during customer evaluations and initial implementations. Work directly with SDKs and APIs to build meaningful integrations into customer environments.
• Build AI Demonstrations & Proofs of Concept:
  Develop mission-focused AI applications and technical demonstrations involving Large Language Models, Agentic AI, Computer Vision, Model inference, Retrieval/RAG architectures, AI/ML pipelines. Demonstrate how AI systems can be attacked, compromised, or manipulated—and how those systems can be protected. Integrate AI security capabilities throughout the ML lifecycle, including model scanning, CI/CD pipelines, application security, runtime detection, and…