AI Infrastructure Engineer

AI Infrastructure EngineerJoin Axon and be a force for good. At Axon, we're on a mission to protect life. We're explorers, pursuing society's 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, you'll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter.Team & Role OverviewAxon's 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.We're 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. You'll 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.We're 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.In this role, you'll: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.What You'll DoProductionize Internal Tools & PrototypesTurn 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 Infrastructure, CI/CD & Platform OperationsOwn 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 Axon's security and compliance expectations.Participate in shared production support and incident response for internal tools and applications.Maintain and Improve Existing SystemsOwn 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.Establish Internal Tool Lifecycle StandardsHelp 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 Applied AI SystemsSupport 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 Axon's internal standards.Collaborate Across AxonWork 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.What You Bring4+ 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, Node.js, 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.Prac