AI Infrastructure Engineer

At Axon were on a mission to Protect Life. Were explorers pursuing societys most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products we work better together. We connect with candor and care seeking out diverse perspectives from our customers communities and each other.

Life at Axon is fast-paced challenging and meaningful. Here youll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter.

Axons Corporate AI Team sits within Business Technology and builds internal-facing AI solutions that help employees reduce manual work move faster and focus on higher-value work. The team develops AI-powered tools internal applications integrations and automation workflows used across Axon.

Were looking for an AI Infrastructure Engineer to help move internal AI and software prototypes from it works to it is production-ready secure reliable supportable and maintainable. This role focuses on the operational backbone of internal applications: infrastructure CI/CD deployment patterns reliability maintenance support and production readiness.

This is a hands-on individual contributor role that blends platform engineering DevOps internal tools engineering and applied AI infrastructure. Youll work closely with Corporate AI IT Enterprise Data Security and business teams to support applications that replace existing software augment workflows and improve how teams operate.

Were open to candidates at multiple levels. This could be a strong platform or DevOps engineer ready to grow into broader ownership or an experienced infrastructure engineer who has operated internal systems at scale.

• Own maintenance support and operational readiness for internal AI-enabled applications and tools.

• Help productionize prototypes built by Corporate AI business teams or technical partners.

• Improve infrastructure and deployment patterns with a focus on Vercel-hosted applications and tools deployed across Azure AWS and other environments.

• Build and maintain CI/CD infrastructure-as-code patterns monitoring secrets management access controls runbooks and support processes.

• Partner through testing rollout UAT and long-term maintenance so internal tools remain useful stable secure and dependable.

This is not an AI research role. You do not need to train models or develop novel ML techniques. You should understand how modern AI-powered applications are built deployed secured monitored and supported in an enterprise environment.

• Turn prototypes proof-of-concepts and team-built tools into reliable applications for real business users.

• Improve production readiness across inherited applications including deployment configuration monitoring error handling documentation testing access controls and supportability.

• Partner with Corporate AI engineers and business teams to move applications from prototype to pilot to production.

• Support UAT and rollout by helping validate that applications meet business needs are stable for daily use and have a clear support model.

• Identify reliability security scalability and maintainability gaps before tools become business-critical.

• Ensure internal applications are not just built but owned supported and continuously improved.

• Own and improve the operational model for internal applications hosted on Vercel including deployment patterns configuration environment management access controls monitoring and production support.

• Support internal applications running across Azure AWS GCP and other cloud environments with Azure experience especially helpful.

• Build and maintain CI/CD workflows primarily using GitHub Actions.

• Apply infrastructure-as-code concepts using Terraform Bicep Pulumi CloudFormation or similar tools.

• Manage platform concerns such as secrets environment variables deployment automation access control logging alerting and operational documentation.

• Partner with IT Security Enterprise Data and Corporate AI to align infrastructure patterns with Axons security and compliance expectations.

• Participate in shared production support and incident response for internal tools and applications.

• Own ongoing maintenance and support for internal applications integrations web apps backend services extensions and workflow tools.

• Fix bugs improve reliability manage dependency updates address security patches and reduce operational toil.

• Improve observability so the team can understand application health usage errors cost and reliability.

• Create runbooks support documentation checklists and escalation paths for applications under Corporate AI ownership.

• Reduce the burden on engineers focused on net-new work by taking ownership of systems that need upkeep and operational care.

• Take pride in brownfield engineering: improving existing systems and making them safer cleaner more reliable and easier to operate.

• Help build a repeatable lifecycle model for internal tools from prototype intake through production readiness support maintenance and retirement.

• Create practical standards such as production readiness checklists UAT checklists CI/CD templates infrastructure patterns runbook templates and support handoff processes.

• Help define what it means for an internal application to be experimental in pilot production-ready business-critical or ready for deprecation.

• Improve how the team inherits supports and maintains applications created by other teams or through rapid prototyping.

• Identify opportunities to consolidate simplify and standardize internal applications and infrastructure over time.

• Support infrastructure and operations for AI-powered internal tools including applications that use LLMs AI agents RAG workflows automation frameworks and enterprise integrations.

• Understand core AI application concepts such as prompt engineering retrieval-augmented generation agentic workflows model APIs evaluations and AI safety considerations.

• Help ensure AI-enabled tools are deployed with appropriate safeguards around data access secrets logging auditability and responsible use.

• Partner with Corporate AI to ensure AI-powered applications are reliable secure supportable and aligned with Axons internal standards.

• Work closely with Corporate AI Enterprise Data IT Security and business stakeholders across Axon.

• Communicate technical risks tradeoffs support concerns and infrastructure needs clearly to technical and non-technical partners.

• Collaborate with teams replacing existing software augmenting workflows or building internal tools to solve business problems.

• Support internal users and stakeholders during rollout support and improvement cycles when needed.

• Bring ownership to ambiguous problems especially when applications have unclear support models incomplete documentation or evolving requirements.
  

• 4 years of experience in platform engineering DevOps infrastructure engineering internal tools engineering automation engineering software engineering or a related technical role.

• Strong cloud infrastructure experience with Azure AWS or GCP; Azure experience is especially helpful.

• Experience with infrastructure-as-code concepts and tools such as Terraform Bicep Pulumi CloudFormation or similar.

• Experience building maintaining or supporting CI/CD pipelines especially with GitHub Actions.

• Strong understanding of deployment patterns environments secrets management access controls monitoring logging and production support.

• Ability to read understand maintain and improve application code in Python TypeScript JavaScript or similar languages.

• Experience supporting production or production-like systems including bug fixes incident response dependency updates documentation and reliability improvements.

• Familiarity with AI application concepts such as LLM APIs prompt engineering RAG agents model evaluation AI security risks and responsible AI practices.

• Strong ownership mindset including comfort taking over work others started bringing order to ambiguity and making systems more reliable over time.

• Strong communication skills and ability to work with technical teams IT partners security stakeholders and internal business users.

• Comfort with brownfield engineering maintenance support and operational excellence.

• Practical service-oriented mindset focused on whether internal tools work well for the people depending on them.
  

You do not need all of these but experience in several areas will help you ramp quickly:

• Operating applications on Vercel including configuration deployments environment variables access controls monitoring and production support.

• Supporting internal tools enterprise applications workflow automation or business-critical internal systems.

• Azure infrastructure identity networking application hosting and security patterns.

• Authentication and authorization patterns such as SSO OAuth/OIDC Entra ID / Azure AD RBAC service principals and secrets management.

• Observability tools logging platforms alerting systems uptime monitoring incident response and operational runbooks.

• Maintaining applications built with Python TypeScript JavaScript React or similar modern stacks.

• Backend services APIs integrations serverless applications containers or cloud-hosted web applications.

• AI-powered internal tools LLM applications RAG workflows agents Slackbots enterprise integrations or automation platforms.

• Integrating with enterprise systems such as Slack Jira Confluence/Quip Microsoft 365 Salesforce Snowflake ServiceNow or similar.

• Working in regulated security-sensitive or compliance-heavy environments.

• Creating engineering standards templates checklists lifecycle models or production readiness frameworks.

• Participating in UAT release readiness stakeholder testing or internal application rollout processes.

• Mentoring or enabling other engineers through documentation templates examples or operational best practices.
  

The ideal candidate is a platform-minded engineer who enjoys taking useful but unfinished software and making it dependable. You may have a background as a DevOps engineer platform engineer infrastructure engineer internal tools engineer automation engineer or software engineer with strong operational instincts.

You are not looking only for greenfield feature work. You are energized by making systems stable maintainable observable secure and easy to support. You are comfortable inheriting prototypes understanding how they work identifying what is missing and building the infrastructure and operational practices needed to make them successful.

You are someone who can say Ill own this and then bring structure to the application deployment support model documentation and long-term maintenance plan.

In your first 30 days you will build context on Axons Corporate AI application landscape understand the current Vercel and cloud footprint meet key partners across Corporate AI IT Enterprise Data and Security and identify the highest-priority reliability and maintenance gaps.

In your first 90 days you will begin owning maintenance for a set of internal applications improve deployment and monitoring patterns for priority tools support at least one application through testing or rollout and establish early production readiness expectations for tools moving beyond prototype stage.

In your first 6 months you will help establish a repeatable internal tool lifecycle program improve the operational model for Vercel-hosted applications harden multiple prototypes or inherited tools into supportable applications and reduce the maintenance burden on engineers focused on net-new development.

This role is for someone who wants to help Axon turn internal AI ideas into dependable software. You will not only help applications get built you will help make sure they keep working remain secure support real users and can be maintained over time.

The best person for this role is a scrappy ownership-oriented platform engineer who cares deeply about reliability infrastructure and operational excellence and who is excited to support the next generation of AI-powered internal tools at Axon.