Axon
Mission Engineer - Baton Rouge
About this role
Axon seeks a Mission Engineer to embed with a strategic law enforcement agency and own end-to-end customer outcomes. You'll design, build, and deploy AI-powered workflows, automations, and integrations on Axon's platform while driving operational transformation and adoption—measured by real production impact, not activity.
What you'll do
- Discover agency problems onsite and translate them into buildable solutions tied to mission outcomes
- Design and ship AI agents, workflows, automations, integrations, and data pipelines using Axon's platform
- Drive hands-on adoption with end users and measure whether changes stick through usage and mission impact
- Build trusted relationships with agency leadership; lead QBRs and strategic planning sessions
- Maintain consistent onsite presence (3-4 days/week) and troubleshoot issues across Product, Engineering, and Support
- Capture field intelligence and turn one-off builds into reusable playbooks for other accounts
What they're looking for
- AI/ML workflow design and deployment (agents, prompts, RAG, evaluations)
- Platform configuration and integration (DEMS, Fusus, Records systems)
- Customer-facing technical leadership and operational transformation
- Problem-solving in complex, mission-critical environments
- Data pipeline and automation architecture
- Change management and adoption strategy
- CJIS compliance and evidence integrity knowledge
- Program management and cross-functional coordination
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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 complex deployment or automation you've shipped into production—what was broken, how did you solve it, and how did you measure impact?
- Describe your experience building AI-powered solutions. How did you approach prompt engineering, evaluation, and iteration based on user feedback?