Senior Software Engineer I, Applied AI

Role details

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. 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‑powered tools. Help define best practices 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 to safely read and write data in Axon systems. Measure and improve reliability, latency, and cost for 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, including prompt engineering, RAG patterns, agentic workflows, and safe tool use. Partner with Corporate AI and Enterprise Architects to shape reference architectures for AI‑enabled apps and workflows (scalability, resiliency, observability, data access). Contribute to internal starter kits, training content, and templates that help other engineers and analysts build on the platform effectively. Security, Governance, and Responsible AI Implement and maintain secure patterns for API key management, secrets, and access to models and data sources, aligned with Axon’s security and compliance standards. Work with Data, Security, and Governance partners to operationalize responsible AI guardrails —e.g., model selection, data minimization, logging, and usage controls. Ensure that new capabilities respect permissions, auditing, and policy requirements across systems like Jira, Quip, Slack, M365, and Snowflake. Collaboration and Communication Collaborate closely with Data Engineering, Solution Architecture, and business teams (Finance, Sales, Operations, etc.) to turn real workflows into repeatable AI solutions. Clearly communicate design decisions, trade‑offs, and status to technical and non‑technical stakeholders. Participate in brainstorming and discovery sessions to identify “big dumb problems worth solving” with AI and automation. What You Bring Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience. 4+ years of professional software engineering experience , including building and operating production services or platforms. Strong proficiency in at least one of: Python, TypeScript/JavaScript, or Java . Experience with at least one major cloud provider (AWS, Azure, or GCP) and comfort working with cloud‑native services (functions, containers, managed databases, queues, storage). Solid understanding of web service fundamentals (APIs, authentication/authorization, observability, CI/CD) and how to build reliable, maintainable systems. Demonstrated interest in AI/ML and LLMs —for example, integrating with OpenAI, Bedrock, or similar APIs; building GPT‑style apps; or using AI‑assisted development tools. Basic understanding of security best practices (secrets management, least‑privilege access, audit logging) and eagerness to deepen this in the context of AI platforms. Strong communication and collaboration skills , with comfort working in a fast‑moving, experimental environment. Preferred Experience You don’t need all of these, but experience in several of the following will help you ramp quickly: Hands‑on work with LLM platforms and tools , such as OpenAI (ChatGPT / APIs), AWS Bedrock, Azure OpenAI, or Azure ML Studio. Experience integrating with enterprise systems like Jira, Confluence/Quip, Slack, M365, Salesforce, or Snowflake —especially via APIs or connectors. Familiarity with RAG (Retrieval‑Augmented Generation) , semantic search, or vector stores in production contexts. Exposure to DevOps / platform engineering : Terraform or similar IaC, GitHub Actions or other CI/CD systems, containerization (Docker, Kuberne