Principal/Senior AI Engineer - Agent Factory

About the Team

Agent Factory is where Workday’s next chapter gets built. We’re forming small, senior, cross-functional AI teams that bring together product leaders, AI engineers, and full-stack builders to create intelligent agents used by millions of people every day. This is production-grade AI—deeply embedded into Workday’s platform—not research experiments or maintenance work. Teams own problems end to end, collaborate tightly across disciplines, and use the right tools to solve real customer challenges at global scale. You’ll work at the intersection of AI, platform architecture, and human workflows, with the autonomy to shape how agents reason, act, and scale responsibly. High trust, high expectations, and real impact. Engineering, but brighter.

About the Role

As a Principal/Senior AI Engineer in Agent Factory, you will drive the end-to-end system design, implementation, and product integration for a core domain of Workday’s next generation of intelligent agents. While our ML Engineers focus on building, training, and optimizing foundational algorithms, your mission is intelligence orchestration and product delivery—connecting the brain to the product.

Sitting at the intersection of AI capabilities, enterprise platforms, and human workflows, you will develop and integrate foundational models safely and reliably into functional, production-grade software. You will be hands-on in the design, experimentation, and orchestration of complex agentic workflows, translating cutting-edge AI capabilities into scalable business value. Because these agents interact with sensitive HR and financial data at a global scale, you will be a key contributor to Responsible and Governed AI—implementing strict guardrails for data privacy, predictability, and explainability within your pod. This role requires a balance of domain-level system architecture and rigorous execution, solving critical product constraints like latency, cost, and reliability.

About You

P5 Principal AI Engineer
Basic Qualifications

• 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.

P4 Sr AI engineer

Basic Qualifications

• 8+ years of professional software engineering experience with strong expertise in backend architecture, distributed systems, and API design, plus 1+ years of dedicated focus building production-grade LLM/agentic systems OR 5+ years of experience specifically within Machine Learning Engineering or AI application development, with 2+ years dedicated to shipping LLM-backed products.
• 2+ years of hands-on experience integrating large models (LLMs, Foundation Models) and modern AI APIs into user-facing enterprise products.
• 1+ years of experience designing and scaling AI orchestration architectures—including multi-agent frameworks, routing layers, or advanced RAG pipelines.
• 4+ years of experience optimizing application performance (specifically tackling constraints like API latency and user interaction design), with 1+ years applied to modern LLM constraints (such as token management, cost optimization, and context-window efficiency).
• 4+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP) to deploy highly responsive, scalable systems.

Other Qualifications

• Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field.
• Responsible AI Implementation: Strong understanding of how to execute governance, guardrails, security layers, and evaluation mechanisms necessary when deploying autonomous agents over sensitive enterprise HR and financial data.
• Technical Leadership & Mentorship: Proven track record of technically leading engineering workstreams within a pod, taking ownership of the development lifecycle, and mentoring junior-to-mid level engineers.
• 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 & Reusability: Proven ability to architect robust application layers that wrap around AI models, establishing reusable patterns for system predictability, error handling, and seamless UX integration.
• Experimentation & Evaluation: Skilled in rapid prototyping, benchmarking model outputs against product requirements, and setting up automated evaluation metrics (e.g., assessing retrieval quality and agentic behavior).
• Thrives in Ambiguity: Highly autonomous builder capable of taking open-ended product goals and breaking them down into concrete, scalable engineering realities.

Workday Pay Transparency Statement

The annualized base salary ranges for the primary location and any additional locations are listed below.  Workday pay ranges vary based on work location. As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants. Recruiters can share more detail during the hiring process. Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things. For more information regarding Workday’s comprehensive benefits, please .

Primary Location: USA.CA.PleasantonPrimary Location Base Pay Range: $246,000 USD - $370,000 USD

 

Additional US Location(s) Base Pay Range: $206,000 USD - $370,000 USD

Our Approach to Flexible Work
 

With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.