Axon
AI Infrastructure Engineer
About this role
Axon's Corporate AI Team seeks an AI Infrastructure Engineer to transform internal AI prototypes into production-ready, secure, and maintainable applications. You'll own infrastructure, CI/CD, deployment patterns, and operational support for internal AI-enabled tools that help employees work more efficiently across the organization.
What you'll do
- Productionize AI prototypes and internal tools, ensuring production readiness, security, reliability, and maintainability
- Own infrastructure and deployment patterns for Vercel-hosted applications and tools across Azure, AWS, and other cloud environments
- Build and maintain CI/CD workflows using GitHub Actions and infrastructure-as-code tools like Terraform or Bicep
- Manage platform concerns including secrets management, environment variables, access controls, logging, monitoring, and alerting
- Partner with Corporate AI, IT, Security, and business teams to support UAT, rollout, and long-term maintenance of internal applications
- Participate in shared production support and incident response for internal tools
What they're looking for
- CI/CD pipeline design and GitHub Actions
- Infrastructure-as-code (Terraform, Bicep, Pulumi, or CloudFormation)
- Cloud platform operations (Azure, AWS, GCP)
- Vercel deployment and configuration
- Secrets management and access control systems
- Monitoring, logging, and alerting tools
- Containerization and orchestration (Docker, Kubernetes preferred)
- Production support and incident response
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Axon
Axon builds software and technology solutions for public safety agencies, including cloud and on-premises platforms for emergency communications, case management, and operational workflows. The company is hiring Implementation Engineers, Mission Engineers, Maintenance Engineers, and Integration Engineers to deploy these systems, optimize client integrations, and ensure reliable operations across diverse public safety environments.
- Website
- axon.com
Likely interview questions
- Tell us about a time you took a prototype or proof-of-concept into production—what challenges did you face and how did you address them?
- Describe your experience with Vercel deployments. How would you approach managing a portfolio of internal applications across Vercel and multiple cloud providers?