Sr. Software Engineer I, Applied AI

Axon Axon · Enterprise · AZ · 4702 Business Intelligence

Axon's Corporate AI Team is seeking a Senior Software Engineer to help design, build, and scale Cortex, their internal AI platform. This role involves owning core services and integrations for AI experiences, collaborating with various teams to create secure AI-powered tools, and defining best practices for LLM and agentic workloads. The engineer will focus on hands-on development, coding, and operating production systems, driving the end-to-end lifecycle of AI features, evaluating new AI services, and implementing monitoring and auditing for AI services. They will also contribute to defining best practices, reference architectures, and starter kits, while ensuring secure patterns and responsible AI guardrails are in place.

What you'd actually do

  1. Design, build, and operate services that power Axon’s internal AI platform (Cortex), including chat experiences, agents, and integrations with enterprise systems.
  2. Implement and standardize patterns for deploying AI-driven applications (APIs, web services, workflows, agents) across Axon’s cloud environments.
  3. Own and extend connectors and MCP integrations (e.g., Jira, Quip, Slack, M365, Snowflake) that allow LLMs to safely read and write data in Axon systems.
  4. Measure and improve reliability, latency, and cost for AI workloads, using telemetry and feedback from real users.
  5. Drive the end-to-end lifecycle of new AI features—from prototype to hardened production service (design, implementation, testing, rollout, and operations).

Skills

Required

  • Python, TypeScript/JavaScript, or Java
  • major cloud provider (AWS, Azure, or GCP)
  • web service fundamentals (APIs, authentication/authorization, observability, CI/CD)
  • integrating with OpenAI, Bedrock, or similar APIs; building GPT-style apps; or using AI-assisted development tools
  • security best practices (secrets management, least-privilege access, audit logging)

Nice to have

  • LLM platforms and tools, such as OpenAI (ChatGPT / APIs), AWS Bedrock, Azure OpenAI, or Azure ML Studio
  • integrating with enterprise systems like Jira, Confluence/Quip, Slack, M365, Salesforce, or Snowflake
  • RAG (Retrieval-Augmented Generation)
  • agentic workflows
  • prompt engineering
  • monitoring, alerting, and auditing for AI services
  • building and operating production services or platforms

What the JD emphasized

  • hands-on senior IC role
  • secure integrations
  • secure patterns
  • responsible AI guardrails
  • security and compliance standards

Other signals

  • building and operating internal-facing AI solutions
  • internal AI platform (Cortex)
  • GPT-class models, chat assistants, and secure integrations
  • design, build, and scale this platform
  • hands-on senior IC role