Principal, Software Engineer (distributed Systems)

Workday Workday · Enterprise · Atlanta, GA +3

Principal Software Engineer on the AI Agent Engineering team, focusing on building and integrating AI agents for HR & Finance within the Workday suite. Responsibilities include leading a team, defining tooling strategy, evaluating AI frameworks, orchestrating agent workflows, and ensuring scalability, reliability, and compliance of AI solutions.

What you'd actually do

  1. 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.
  2. Understanding of AI Lifecycle: Extensive knowledge of the AI system lifecycle, including problem definition, data acquisition, model training, system integration, and validation
  3. Evaluate, select, and integrate AI tools, frameworks, and platforms to ensure scalability, efficiency, and compliance
  4. Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
  5. Work with product, engineering, and data science teams to innovate and implement AI-based automation solutions that enhance HR and financial operations.

Skills

Required

  • 8+ years of experience with product engineering leading the development and delivery of highly available cloud products
  • 5+ years proven experience in AI, machine learning, or intelligent automation, with a focus on enterprise applications.
  • 2+ years experience with LLMs, AI agents, and orchestration frameworks (e.g. LangGraph)
  • 5+ years experience with Python, cloud AI services (AWS, Azure, GCP), and AI model deployment.

Nice to have

  • Bachelor's degree or equivalent experience 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 track record to tackle sophisticated business challenges by translating them into innovative, AI-powered solutions that drive measurable results
  • Experience with 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 AI models including identifying and curating datasets as well as experimenting with models for iterative improvement

What the JD emphasized

  • AI Agent Engineering team
  • AI Agents
  • AI Agent Principal Engineer
  • AI solutions
  • AI frameworks
  • agent workflows
  • LLMs
  • automation frameworks
  • enterprise AI technologies
  • AI agents
  • AI Lifecycle
  • AI system lifecycle
  • AI tools
  • AI advancements
  • LLMs
  • autonomous agents
  • orchestration frameworks
  • AI-based automation solutions
  • AI vendors
  • AI stack
  • AI technologies
  • responsible AI
  • Agent Factory pipeline
  • AI, machine learning, or intelligent automation
  • enterprise applications
  • LLMs
  • AI agents
  • orchestration frameworks
  • AI model deployment
  • enterprise-grade AI architectures
  • large-scale automation
  • vector databases
  • retrieval-augmented generation (RAG)
  • fine-tuning LLMs
  • AI-powered solutions
  • data privacy, security, and compliance for AI
  • machine learning solutions
  • large-scale datasets
  • model development
  • fine-tuning AI models

Other signals

  • AI Agents
  • LLMs
  • Orchestration
  • Enterprise AI