Senior Software Development Engineer - AI Core

Workday Workday · Enterprise · Vancouver, BC

Senior Software Development Engineer on the AI Core team, responsible for designing, building, and operating software systems for AI-powered agentic applications. Focuses on building agentic tooling for information retrieval using ML RAG platforms, with an emphasis on enterprise-scale systems for retrieval and recommendation use cases.

What you'd actually do

  1. Design and implement production-grade services, APIs, and ETL pipelines in the ML RAG platform, which is consumed by numerous AI-driven Workday products and high-priority agents
  2. Apply distributed systems principles in production to address scalability, concurrency, fault tolerance, and performance challenges
  3. Automate CI/CD and testing workflows, and proactively look for ways to improve developer experience
  4. Ensure systems meet enterprise requirements for security, privacy, robustness, and compliance
  5. Own services through their full lifecycle, including deployment, monitoring, debugging, and ongoing operational improvements

Skills

Required

  • Python development
  • architecting, building, and scaling secure, robust, and efficient software systems
  • Python expertise
  • solid software engineering skills
  • advanced Python concepts such as asynchronous and concurrent programming, generators, higher-order abstractions, Pydantic and Pyspark
  • Deep systems knowledge
  • Unix/Linux environments
  • command-line tooling
  • practical networking fundamentals
  • distributed systems concepts
  • cloud and container platforms
  • containerized workloads and orchestration systems (e.g., AWS or GCP, Docker, Kubernetes)
  • collaborate effectively across teams
  • Ownership mindset
  • Architectural thinking skills
  • communicate complex technical concepts clearly

Nice to have

  • building scalable services and pipelines for ML use cases in Production

What the JD emphasized

  • production-grade services
  • ML RAG platform
  • enterprise requirements for security, privacy, robustness, and compliance
  • Python expertise
  • solid software engineering skills
  • architecting, building, and scaling secure, robust, and efficient software systems
  • building scalable services and pipelines for ML use cases in Production
  • Deep systems knowledge
  • distributed systems concepts
  • cloud and container platforms
  • containerized workloads and orchestration systems

Other signals

  • designing, building, and operating the software systems that host, run, and scale AI-powered agentic applications
  • build the agentic tooling for both unstructured and structured information retrieval
  • ML RAG platform
  • enterprise-scale systems for information retrieval and recommendation use cases