Senior Software Engineer - AI Tools

Workday Workday · Enterprise · Pleasanton, CA +1

Senior Software Engineer to design and build the AI platform layer, including LLM gateways, MCP registries, abstraction APIs, and shared infrastructure. The role involves architecting and implementing AI agents and automation to improve developer workflows, evaluating and integrating AI model providers, and building observability and governance tooling for responsible AI use in an enterprise setting.

What you'd actually do

  1. Design and build the AI platform layer: LLM gateways, MCP registries, abstraction APIs, and shared infrastructure that teams across Workday build on
  2. Architect and implement AI agents and automation that improve the developer workflow - code review, documentation, testing, and beyond
  3. Drive the evaluation and integration of AI model providers, keeping a strong eye on cost, safety, and performance tradeoffs
  4. Serve as a scout for emerging AI capabilities - track new models, tools, and frameworks, and make recommendations to engineering leadership on when to adopt, evaluate, or pass
  5. Lead technical design on complex, cross-cutting projects in partnership with Security, DevEx, and infrastructure teams

Skills

Required

  • 5+ years of software engineering experience
  • 3+ years building and operating backend services or platform infrastructure in production
  • Strong proficiency in Python and/or TypeScript
  • Hands-on experience building or integrating with LLM APIs (OpenAI, Anthropic, etc.)
  • familiarity with prompt engineering, evaluation pipelines, and model selection tradeoffs
  • Robust understanding of AI security principles and responsible AI practices - including prompt injection, model misuse, data leakage risks, output validation, and operationalizing policy guardrails in production AI systems
  • Genuine excitement about being on the leading edge of AI

Nice to have

  • Hands-on experience designing and building AI agents, including fluency with agentic frameworks and patterns such as ReAct
  • practical experience shipping agents that are reliable in production
  • Experience building MCP servers, Claude skills, or similar tool-use integrations that extend AI capabilities to real workflows
  • Comfort designing REST APIs and service-oriented architectures
  • Experience with container orchestration (Kubernetes, Docker) and deploying services to cloud environments
  • experience with workflow orchestration tools and infrastructure-as-code is a plus
  • Understanding of enterprise security patterns: authentication, authorization, SSO, and secrets management
  • Exposure to observability tooling - logging, metrics, tracing
  • habit of building systems that are easy to monitor
  • Comfort partnering directly with the engineers who use your tools
  • Strong written and verbal communication skills
  • able to drive alignment across engineering, security, and product stakeholders

What the JD emphasized

  • building and operating backend services or platform infrastructure in production
  • Hands-on experience building or integrating with LLM APIs
  • Robust understanding of AI security principles and responsible AI practices
  • Hands-on experience designing and building AI agents
  • practical experience shipping agents that are reliable in production

Other signals

  • AI platform layer
  • AI agents
  • developer experience
  • LLM gateways
  • operationalize AI