Sr. Software Engineer I, Applied AI

Join 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. Sr SWE -AI Senior Software Engineer, AI Platform (Corporate AI Team) Team & Role Overview Axon’s Corporate AI Team sits within the Enterprise Data organization and focuses on internal-facing AI solutions that help Axon employees do more high‑value work with less manual effort. The team builds and operates Cortex, Axon’s internal AI platform that provides GPT‑class models, chat assistants, and secure integrations with systems like Quip, Jira, Slack, Microsoft 365, Snowflake, and more. We’re looking for a Senior Software Engineer, AI Platform to help design, build, and scale this platform. In this role, you’ll: Own core services and integrations that power AI experiences for thousands of Axon employees. Collaborate with Corporate AI, Data Engineering, and business teams to turn real workflows into secure, reliable AI‑pow [... source excerpt omitted ...] es and patterns for building production‑grade LLM and agentic workloads at Axon. This is a hands‑on senior IC role—you’ll spend most of your time designing, coding, and operating production systems, not managing people. What You’ll Do Build and Evolve the AI Platform Design, build, and operate services that power Axon’s internal AI platform (Cortex), including chat experiences, agents, and integrations with enterprise systems. Implement and standardize patterns for deploying AI‑driven applications (APIs, web services, workflows, agents) across Axon’s cloud environments. Own and extend connectors and MCP integrations (e.g., Jira, Quip, Slack, M365, Snowflake) that allow LLMs t [... source excerpt omitted ...] AI workloads, using telemetry and feedback from real users. Deployment, Integration, and Reliability Drive the end‑to‑end lifecycle of new AI features—from prototype to hardened production service (design, implementation, testing, rollout, and operations). Evaluate new AI services (e.g., OpenAI, AWS Bedrock, Azure ML) for fit, performance, and cost; recommend and implement the right options for each use case. Implement monitoring, alerting, and auditing for AI services, ensuring we can trace usage, detect anomalies, and keep systems healthy in production. Enablement, Best Practices, and Architecture Help define and document best practices for GPT/LLM solution development, inc