Sr. Software Development Engineer / Principal Software Development Engineer

About the Team

The AI Model Serving team is the engine behind every production Workday agent and machine learning use case. We own the services that power all production AI workloads, serving as the gateway to vendor-hosted LLMs on GCP and AWS Bedrock and operating the model deployment platform where Workday hosts and scales its models.

We host thousands of traditional ML models across sharded Ray Serve clusters. Our platform handles approximately 2,000 requests per second and peaks at greater than 10,000 requests per second in our largest cluster. We also provide a uniform interface for accessing models on Bedrock and Gemini, and the production model registry for Workday.

In the year ahead, we are focused on making our uniform vendor interface more robust, scaling our architecture to support more than 20 agents going into production, improving cost governance, exploring hosting open-source LLMs in our stack, evaluating a potential rewrite of the service in a different language for performance gains, and keeping pace with the rapidly evolving AI landscape.

Our culture is one of focus, camaraderie, and high performance. We are a friendly and dedicated group that takes pride in building and operating one of the most heavily used services at Workday. If you are energized by working on infrastructure that sits at the heart of Workday’s AI strategy, this is the team for you.

About the Role

As either a Senior Software Development Engineer or a Principal Software Development Engineer on the AI Model Serving team, you will be a technical leader who helps shape the vision and direction of the platform alongside the engineering manager. You will play a central role in making critical design decisions, driving outcomes across the team, and setting a positive and inclusive team culture. In addition to the model serving platform, the team also owns the production model registry at Workday, and you will help guide its evolution and ensure it meets the needs of ML teams across the organization.

Your work will directly impact Workday’s ability to serve AI at scale — from traditional ML models to the latest large language models powering Workday’s agents.

Key Responsibilities:

• Lead the team technically by making critical design decisions that drive performance, reliability, and scalability across the platform.
• Design, implement, and maintain large-scale systems that enable moving ML models to production.
• Write design documents to build consensus for new system components and enhancements to existing components.
• Evaluate and uptake new technologies made available within Workday and across the broader industry.
• Troubleshoot, improve, and scale continuous integration software pipelines.
• Develop relationships with software engineers, machine learning engineers, and data scientists on partner teams.
• Respond to alerts and debug production issues to maintain platform health and reliability.
• Review pull requests and enforce consistency, performance, readability, and security across code bases.
• Develop documentation to share knowledge with other engineers.

About You

(P5) Principal SDE Basic Qualifications

• 10+ years of related work experience in software development, with a focus on building and operating large-scale distributed systems.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent practical experience).

Other Qualifications

• Software Development and Distributed Systems: Deep experience designing, building, and scaling production-grade distributed systems. You understand the full software development lifecycle — from coding standards and testing to code reviews, source control management, and deployment — and you can apply that knowledge to complex, high-throughput platforms.
• Python: Deep proficiency in Python, with extensive experience writing production-level code and building systems in Python-based frameworks.
• LLMs and Traditional ML Models: Familiarity with both large language models and traditional machine learning models, including how they are served, scaled, and monitored in production environments. You understand the operational differences and can design platform abstractions that serve both effectively.
• You can design and maintain monitoring strategies that provide clear insight into system health, performance, and cost.
• Excellent written and verbal communication skills, including the ability …