WillHire

Principal Data Engineer

WillHire

full-time

Posted on:

Location: California, Colorado • 🇺🇸 United States

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Salary

💰 $230,400 - $345,600 per year

Job Level

Lead

Tech Stack

AWSAzureCloudGoogle Cloud PlatformPython

About the role

  • Join the AI Agent Engineering team pioneering HR & Finance AI Agents integrated within Workday suite.
  • Architect and build foundational evaluation datasets and blueprints for agentic workflows.
  • Craft, refine, and iterate evaluation datasets to create measurable benchmarks for continuous improvement.
  • Define standards and build a scalable evaluation machine as the backbone of agentic workflows across the company.
  • Collaborate across teams to ensure datasets capture complexities of real-world scenarios.

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.
  • 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
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