Senior AI Engineer

Workday•Atlanta, GA

1d•$176,000 - $312,000•Hybrid

About The Position

As a 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.

Requirements

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.
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.
Proven track record of technically leading engineering workstreams within a pod, taking ownership of the development lifecycle, and mentoring junior-to-mid level engineers.
Deep focus on business value, user experience, and applying deep learning/large models directly to solve practical end-user challenges.
Proven ability to architect robust application layers that wrap around AI models, establishing reusable patterns for system predictability, error handling, and seamless UX integration.
Skilled in rapid prototyping, benchmarking model outputs against product requirements, and setting up automated evaluation metrics (e.g., assessing retrieval quality and agentic behavior).
Highly autonomous builder capable of taking open-ended product goals and breaking them down into concrete, scalable engineering realities.

Nice To Haves

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

Responsibilities

Drive the end-to-end system design, implementation, and product integration for a core domain of Workday’s next generation of intelligent agents.
Connect the AI 'brain' to the product for intelligence orchestration and product delivery.
Develop and integrate foundational models safely and reliably into functional, production-grade software.
Be hands-on in the design, experimentation, and orchestration of complex agentic workflows.
Translate cutting-edge AI capabilities into scalable business value.
Implement strict guardrails for data privacy, predictability, and explainability within your pod.
Solve critical product constraints like latency, cost, and reliability.
Technically lead engineering workstreams within a pod.
Take ownership of the development lifecycle.
Mentor junior-to-mid level engineers.
Architect robust application layers that wrap around AI models.
Establish reusable patterns for system predictability, error handling, and seamless UX integration.
Prototype rapidly, benchmark model outputs against product requirements, and set up automated evaluation metrics.