AI Engineer
Posted Updated
Description
- As a Machine Learning Engineer in Agent Factory, you’ll design and build the core ML systems behind Workday’s next generation of AI agents. Working within a small, senior, cross-functional pod, you’ll own how models, agent logic, and orchestration layers come together in production—across the full lifecycle from problem framing and data strategy to deployment, monitoring, and continuous improvement
- You’ll implement and evolve frameworks for LLM-powered agents, including RAG pipelines, workflow orchestration, evaluation, and feedback loops, ensuring solutions are scalable, observable, and enterprise-ready
- This role sits at the intersection of ML and platform engineering: partnering closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack
- You’ll stay hands-on with emerging techniques in agentic architectures while applying strong engineering judgment to turn them into systems that are reliable, explainable, and built to operate at global scale
- Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
- 5+ years experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale, including taking products through applied research, design, implementation, production, and production-based evaluation
- 1+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
- 3+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
- 3+ years of professional experience in building services to host machine learning models in production at scale
- 2+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
- Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
- Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases
- Professional experience in independently solving ambiguous, open-ended problems and technically leading teams
- Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders