Principal AI Engineer

WillHire

Principal AI Engineer at Workday designing systems for the next generation of intelligent agents. Leading product integration and ensuring Responsible AI standards are met in high-scale environments.

Posted 7/16/2026full-timePleasanton • California, Colorado, Washington • 🇺🇸 United StatesLead💰 $246,000 - $370,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and integrating large language models and AI systems into enterprise applications, with a strong focus on Responsible AI practices and optimizing performance constraints. Proven ability to lead cross-functional teams and drive product development from concept to deployment while ensuring user experience and business value.

Highest-signal resume keywords

Large Language Model IntegrationAI Orchestration Architecture DesignCloud Computing (AWS, GCP)Responsible AI StewardshipCross-Functional Team Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed SystemsAPI DesignMachine Learning EngineeringPerformance OptimizationError HandlingRapid PrototypingBenchmarkingContext-Window EfficiencyToken ManagementComplex Workflow Design

Soft Skills

MentoringAutonomous LeadershipUser Experience FocusCollaborationProblem Solving

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMaster’s Degree Preferred

Industry Keywords

Responsible AIData PrivacyGovernanceEnterprise SoftwareAI Capabilities

Tech Stack

Tools & technologies

AWSCloudDistributed SystemsGoogle Cloud Platform

About the role

Key responsibilities & impact

Lead the end-to-end system design, architectural framework, and product integration of Workday’s next generation of intelligent agents.
Architect how foundational models are safely and reliably integrated into functional, production-grade software.
Own the design, experimentation, and orchestration of complex agentic workflows, translating cutting-edge AI capabilities into enterprise-grade business value.
Be a core champion for Responsible and Governed AI—architecting systems with strict guardrails for data privacy, predictability, and explainability.
Balance high-level system design and hands-on execution, solving critical product constraints like latency, cost, and reliability.

Requirements

What you’ll need

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.
Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field.
Deep understanding of Responsible AI Stewardship including 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.
Deep focus on business value, user experience, and applying deep learning/large models directly to solve practical end-user challenges.
Expert-level ability to architect robust application layers that wrap around AI models, ensuring system predictability, error handling, and seamless UX integration.
Skilled in rapid prototyping, benchmarking model outputs against product requirements, and managing continuous experimentation cycles for agentic behavior.
Highly autonomous leader capable of taking complex, open-ended product goals and turning them into scalable, concrete engineering realities.

Benefits

Comp & perks

Workday Bonus Plan
Annual refresh stock grants