Principal AI Engineer

Workday Workday · Enterprise · Prague, Czech Republic

Principal AI Engineer to lead end-to-end system design, architectural framework, and product integration of intelligent agents. Focus on intelligence orchestration and product delivery, connecting foundational models to enterprise products. Architecting systems with strict guardrails for data privacy, predictability, and explainability, balancing system design with hands-on execution to solve latency, cost, and reliability constraints.

What you'd actually do

  1. Lead the end-to-end system design, architectural framework, and product integration of Workday’s next generation of intelligent agents.
  2. Own the design, experimentation, and orchestration of complex agentic workflows, translating cutting-edge AI capabilities into enterprise-grade business value.
  3. Architect how foundational models are safely and reliably integrated into functional, production-grade software.
  4. Architect systems with strict guardrails for data privacy, predictability, and explainability.
  5. Solve critical product constraints like latency, cost, and reliability.

Skills

Required

  • 10+ years of professional software engineering experience with deep expertise in distributed systems, cloud computing, and API design, plus 2+ years of dedicated focus building production-grade LLM/agentic systems OR 7+ years of experience specifically within Machine Learning Engineering or AI application development, with 3+ years dedicated to shipping LLM-backed products.
  • 3+ years of hands-on experience integrating large models (LLMs, Foundation Models) and modern AI APIs into user-facing enterprise products.
  • 2+ years of experience designing and scaling complex AI orchestration architectures—including multi-agent frameworks, routing layers, and advanced RAG pipelines.
  • 6+ years of experience optimizing application performance (specifically tackling constraints like API latency and user interaction design), with 2+ years applied to modern LLM constraints (such as token management, cost optimization, and context-window efficiency).
  • 6+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP) to deploy highly responsive, scalable systems.
  • Responsible AI Stewardship: Deep understanding of how to implement governance, guardrails, security layers, and evaluation mechanisms necessary when deploying autonomous agents over sensitive enterprise HR and financial data.
  • Proven track record of technically leading cross-functional pods, mentoring senior engineers, and steering the product development lifecycle from abstract concept to successful deployment.
  • Product-First AI Mindset: Deep focus on business value, user experience, and applying deep learning/large models directly to solve practical end-user challenges.
  • System Design & Integration: Expert-level ability to architect robust application layers that wrap around AI models, ensuring system predictability, error handling, and seamless UX integration.
  • Experimentation & Evaluation: Skilled in rapid prototyping, benchmarking model outputs against product requirements, and managing continuous experimentation cycles for agentic behavior.
  • Thrives in Ambiguity: Highly autonomous leader capable of taking complex, open-ended product goals and turning them into scalable, concrete engineering realities.

Nice to have

  • Master’s degree in Computer Science, Software Engineering, or equivalent technical field.

What the JD emphasized

  • production-grade LLM/agentic systems
  • shipping LLM-backed products
  • complex AI orchestration architectures
  • advanced RAG pipelines
  • tackling constraints like API latency
  • modern LLM constraints
  • Responsible AI Stewardship
  • guardrails
  • governance
  • evaluation mechanisms
  • autonomous agents
  • Product-First AI Mindset
  • deep learning/large models directly to solve practical end-user challenges
  • System Design & Integration
  • Experimentation & Evaluation
  • continuous experimentation cycles for agentic behavior

Other signals

  • production-grade AI
  • intelligent agents
  • orchestration
  • enterprise-grade business value
  • Responsible and Governed AI
  • global scale