Principal/Senior AI Engineer - Agent Factory

Workday • Pleasanton, CA

1d • Hybrid

About The Position

Agent Factory forms small, senior, cross-functional AI teams that build production-grade intelligent agents embedded in Workday’s platform. The teams own customer problems end to end, bringing together product leaders, AI engineers, and full-stack builders to create agents used at global scale. The work sits at the intersection of AI, platform architecture, and human workflows, with autonomy to shape how agents reason, act, and scale responsibly.

As a Principal/Senior AI Engineer, you will drive end-to-end system design, implementation, and product integration for a core domain of Workday’s next generation of intelligent agents. The role focuses on intelligence orchestration and product delivery: connecting AI capabilities to enterprise products and human workflows, and safely and reliably integrating foundational models into production-grade software. You will design, experiment with, and orchestrate complex agentic workflows, turning AI capabilities into scalable business value.

Because the agents handle sensitive HR and financial data, you will contribute to Responsible and Governed AI by implementing guardrails for privacy, predictability, and explainability. The role combines domain-level system architecture with hands-on engineering execution, while addressing constraints such as latency, cost, and reliability.

Requirements

• For the Principal level: 10+ years of professional software engineering experience with deep expertise in distributed systems, cloud computing, and API design, plus 2+ years building production-grade LLM/agentic systems; alternatively, 7+ years specifically in Machine Learning Engineering or AI application development, including 3+ years shipping LLM-backed products.
• For the Principal level: 3+ years integrating large models (LLMs or foundation models) and modern AI APIs into user-facing enterprise products.
• For the Principal level: 2+ years designing and scaling complex AI orchestration architectures, including multi-agent frameworks, routing layers, and advanced RAG pipelines.
• For the Principal level: 6+ years optimizing application performance, including API latency and user interaction design, with 2+ years addressing modern LLM constraints such as token management, cost optimization, and context-window efficiency.
• For the Principal level: 6+ years using cloud computing platforms such as AWS or GCP to deploy highly responsive, scalable systems.
• For the Senior level: 8+ years of professional software engineering experience with strong expertise in backend architecture, distributed systems, and API design, plus 1+ years building production-grade LLM/agentic systems; alternatively, 5+ years specifically in Machine Learning Engineering or AI application development, including 2+ years shipping LLM-backed products.
• For the Senior level: 2+ years integrating large models (LLMs or foundation models) and modern AI APIs into user-facing enterprise products.
• For the Senior level: 1+ years designing and scaling AI orchestration architectures, including multi-agent frameworks, routing layers, or advanced RAG pipelines.
• For the Senior level: 4+ years optimizing application performance, including API latency and user interaction design, with 1+ years addressing modern LLM constraints such as token management, cost optimization, and context-window efficiency.
• For the Senior level: 4+ years using cloud computing platforms such as AWS or GCP to deploy highly responsive, scalable systems.
• Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
• Strong understanding of governance, guardrails, security layers, and evaluation mechanisms for deploying autonomous agents over sensitive enterprise HR and financial data.
• Proven ability to technically lead engineering workstreams, own the development lifecycle, and mentor junior-to-mid level engineers.
• Strong product-first focus on business value, user experience, and applying deep learning or large models to practical end-user challenges.
• Ability to architect robust application layers around AI models and establish reusable patterns for predictability, error handling, and seamless UX integration.
• Skill in rapid prototyping, benchmarking model outputs against product requirements, and setting up automated evaluation metrics, including retrieval quality and agentic behavior.
• Ability to work autonomously with open-ended product goals and turn them into concrete, scalable engineering solutions.

Nice To Haves

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

Responsibilities

• Drive end-to-end system design, implementation, and product integration for a core domain of intelligent agents.
• Integrate foundational models and AI capabilities safely and reliably into functional, production-grade software.
• Design, experiment with, and orchestrate complex agentic workflows.
• Translate AI capabilities into scalable business value and solve practical end-user challenges.
• Implement responsible AI governance and guardrails for data privacy, predictability, and explainability.
• Address product constraints including latency, cost, and reliability.
• Technically lead engineering workstreams within a pod and take ownership of the development lifecycle.
• Mentor junior-to-mid level engineers.
• Prototype and benchmark model outputs against product requirements and establish automated evaluation metrics.
• Architect reusable application patterns for predictability, error handling, and seamless user experience integration.

Benefits

• May be eligible for the Workday Bonus Plan or a role-specific commission/bonus.
• May be eligible for annual refresh stock grants.
• Comprehensive benefits are referenced, but specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior