The ad says“4+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)”
The ad says“2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases”
The ad says“2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases”
The ad says“4+ years of professional experience in building services to host machine learning models in production at scale”
The ad says“Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases”
The ad says“3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow”
The ad says“Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases”
The ad says“Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases”
The ad says“3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow”
The ad says“2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases”
The ad says“Deep understanding of statistical analysis, unsupervised and supervised machine learning algorithms, and natural language processing for information retrieval and/or recommendation system use cases”
As an AI 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
Preferred experience
2+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
4+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
4+ years of professional experience in building services to host machine learning models in production at scale
3+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent
Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement
7+ years experience as a member of an AI engineer, 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
Stay up to date with advancements in AI, LLMs, RAG, autonomous agents and orchestration frameworks to drive innovation
Excellent interpersonal and communication skills, with the ability to build strong relationships across teams and stakeholders
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
