Software Development Engineer - AI Tools

Workday Workday · Enterprise · Pleasanton, CA

Software Development Engineer focused on building and maintaining the AI platform infrastructure for enterprise AI tools, including code generation, AI-assisted code review, and agentic workflows. The role involves developing backend services, integrations, and developer tooling, with a focus on operationalizing AI responsibly and ensuring observability.

What you'd actually do

  1. Build and maintain MCP servers, AI integrations, and developer tools that improve the daily workflow for Workday's engineering org
  2. Write scripts and automation that streamline AI tool onboarding, configuration, and fleet management
  3. Instrument services with logging and monitoring to keep our platform observable and reliable
  4. Work with Security and compliance partners to implement policy requirements in our tooling
  5. Participate in code review and help maintain a high standard for the team's codebase

Skills

Required

  • 3+ years of software engineering experience
  • Production experience building backend services or developer tooling in Python and/or TypeScript
  • Familiarity with LLM APIs and basic prompt engineering concepts
  • Working understanding of AI security and responsible AI principles - including awareness of prompt injection, data handling risks, and how to build AI integrations that respect policy and compliance boundaries
  • Genuine excitement about the cutting edge of AI - you stay current on new tools, models, and frameworks, and you're eager to bring what you're learning into your day-to-day work

Nice to have

  • Understanding of AI agent architectures, including the ReAct pattern and how agents reason, plan, and use tools iteratively; hands-on experience building or experimenting with agents is a plus
  • Experience building MCP servers, skills, or tool-use integrations; comfort reading and contributing to the MCP ecosystem
  • Experience building or consuming REST APIs
  • Working knowledge of Docker and container-based deployments; experience with Kubernetes is a plus, and exposure to workflow orchestration or infrastructure-as-code tooling is welcome
  • Familiarity with DevOps practices and CI/CD systems
  • Understanding of authentication and authorization fundamentals (OAuth, SSO, API keys)
  • Comfort working in a cloud environment (AWS, GCP, or Azure)
  • Experience adding observability to services - logging, metrics, alerting
  • Comfort partnering directly with the engineers who use your tools - you're happy to answer questions, support adoption, and treat user feedback as input, not interruption
  • Strong written communication skills; able to document your work clearly for other developers

What the JD emphasized

  • production code
  • high-impact
  • high-ownership
  • policy requirements
  • compliance boundaries
  • AI agent architectures
  • agents reason, plan, and use tools iteratively
  • authentication and authorization fundamentals

Other signals

  • AI platform layer
  • code generation
  • AI-assisted code review
  • agentic workflows
  • enterprise-wide developer tooling
  • operationalize how AI is used responsibly