Senior Software Engineer, AI Platform

About the Role

Axon’s Corporate AI Team, within the Enterprise Data organization, focuses on internal-facing AI solutions to enhance employee productivity. The team is responsible for building and operating Cortex, Axon’s internal AI platform that provides GPT-class models, chat assistants, and secure integrations with systems like Quip, Jira, Slack, Microsoft 365, and Snowflake.

We are seeking a Senior Software Engineer, AI Platform to design, build, and scale this critical platform. This is a hands-on senior IC role, primarily focused on designing, coding, and operating production systems.

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, Kubernetes), and application monitoring stacks.
• Experience building internal platforms or self-service tools used by multiple teams.
• Participation in enablement activities (internal trainings, office hours, documentation, or community programs like Axon’s Champions Circle) is a plus.