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Principal AI Agent Engineer
| Company | Workday |
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| Location | Boulder, CO, USA |
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| Salary | $193500 – $290300 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Senior, Expert or higher |
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Requirements
- 8+ years of experience with product engineering leading the development and delivery of highly available cloud products
- 5+ years of experience in AI, machine learning, or intelligent automation, with a focus on enterprise applications.
- Deep understanding of LLMs, AI agents, and orchestration frameworks (e.g. LangGraph)
- Proficiency in Python, cloud AI services (AWS, Azure, GCP), and AI model deployment.
Responsibilities
- Lead a high performing team of innovative engineers to deliver AI-powered agents that integrate deeply into HR and Financial workflows, accelerating intelligent decision making.
- Understanding of AI Lifecycle: Comprehensive knowledge of the AI system lifecycle, including problem definition, data acquisition, model training, system integration, and validation
- Evaluate, select, and integrate AI tools, frameworks, and platforms to ensure scalability, efficiency, and compliance
- Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
- Work with product, engineering, and data science teams to design and implement AI-based automation solutions that enhance HR and financial operations.
- Collaborate with external AI vendors, cloud providers, and open-source communities to bring the best-in-class technologies into our AI stack.
- Establish monitoring, feedback loops, and continuous learning mechanisms to improve agent performance over time.
Preferred Qualifications
- Bachelor’s degree in a relevant field, such as Computer Science, Mathematics, or Engineering.
- Experience with enterprise-grade AI architectures, API integration, and large-scale automation.
- Hands-on experience with vector databases, retrieval-augmented generation (RAG), and fine-tuning LLMs.
- Proven ability to solve complex business challenges by translating them into innovative, AI-powered solutions that drive measurable results
- Experience in data privacy, security, and compliance for AI in enterprise environments
- Experience developing and deploying machine learning solutions using large-scale datasets, including specification design, data collection and labeling, model development, validation, deployment, and ongoing monitoring.
- Experience with fine-tuning models including identifying and curating datasets as well as experimenting with models for iterative improvement
