Senior ML Engineer: LLMs, Production AI & IR Systems

Workday; Canada; On-site; CAD 100,000 - 140,000; Full time

Job summary

Workday is seeking an experienced ML Engineer to join the FIN Hyperautomation AI team. You will develop intelligent user experiences with generative AI and work across Workday's ML infrastructure—from training models to APIs and lifecycle management.

The role emphasizes applying ML techniques for intelligent document processing, staying current with LLMs and RAG, and delivering impactful customer workflows at scale.

Qualifications

• 5+ years in data science or ML engineering building ML products at scale.
• Bachelor's degree in Engineering, CS, Physics or Math; Master’s/PhD preferred.
• 5+ years cloud platforms (AWS, GCP) experience.
• 5+ years Python with numeric libraries; shipping production code.
• Strong NLP, with focus on information retrieval or recommendations.
• 3+ years developing with LLMs, RAG, and AI agents.
• 5+ years building scalable ML services on Kubernetes.
• 5+ years ML frameworks such as scikit-learn, PyTorch, TensorFlow.

Responsibilities

• Own exploration, design, implementation, and deployment of features.
• Evaluate, scale, and observe these features in production.
• Apply ML techniques including LLMs to process business documents and automate workflows.
• Stay up to date with AI advances to drive innovation and best practices.
• Serve as a technical role model for junior engineers and collaborate across teams.

Skills

Team leadership

Production ML

Python programming

Statistical analysis

Natural language processing

Information retrieval

Ambiguity problem solving

Education

Bachelor's degree in Engineering / Data/CS / Physics / Math

Master's or PhD preferred

Tools

AWS

GCP

Kubernetes

PyTorch

TensorFlow

scikit-learn

Pandas

PySpark

Job description

Workday is seeking an experienced ML Engineer to join the FIN Hyperautomation AI team. You will develop intelligent user experiences with generative AI and work across Workday's ML infrastructure—from training models to APIs and lifecycle management.

The role emphasizes applying ML techniques for intelligent document processing, staying current with LLMs and RAG, and delivering impactful customer workflows at scale.