Senior AI Engineer in Vancouver

Your work days are brighter here. We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most. Our culture is rooted in integrity, empathy, and shared enthusiasm. We look for curious minds and courageous collaborators who bring sun‑drenched optimism and drive.

About the Team
Join the Evisort AI team at Workday, which powers Document Intelligence AI and Workday's CLM and Contract Intelligence offerings. Our mission is to change the way business deals get done. We build groundbreaking AI technology that can read and understand contract language to make every part of the deal‑making process happen faster, better, with reduced risks. You will be joining the Evisort AI team, which functions as a startup within Workday.

About the Role
As an AI Engineer, you will help develop tailored user experiences using advanced LLMs, Knowledge Graphs, personalization, and predictive analysis. You will collaborate with other engineers to deliver AI solutions across Workday's product ecosystem and utilize software and data engineering stacks to enable training, deployment, and lifecycle management of various AI pipelines. You will develop and deploy new products at scale and leverage Workday's vast computing resources on rich datasets to deliver transformative value to our customers.

Basic Qualifications
8+ years of professional experience in software engineering and product‑building, with a proven track record of shipping and maintaining production code at scale.
3+ years of hands‑on experience integrating large models (LLMs, Foundation Models) and modern AI APIs into user‑facing enterprise products.
1–2+ years of hands‑on experience building with modern AI orchestration frameworks (e.g., LangChain, LlamaIndex, or multi‑agent frameworks) and implementing advanced prompt engineering techniques to manage complex agentic workflows.
4+ years of experience optimizing application performance, specifically tackling product constraints such as API latency, token management, and user interaction design.
4+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP) to deploy highly responsive, scalable systems.
Hands‑on experience deploying and operating services in containerized, cloud‑native environments (Docker, Kubernetes), with exposure to infrastructure‑as‑code and GitOps tooling (e.g., Terraform, Helm, ArgoCD).

Other Qualifications
Bachelor’s degree (Master’s preferred) in Computer Science, Software Engineering, or equivalent technical field.
Product‑First AI Mindset: Deep focus on business value, user experience, and applying deep learning or large models directly to solve practical end‑user challenges.
System Design & Reusability: Proven ability to architect robust application layers that wrap around AI models, establishing reusable patterns for system predictability, error handling, and seamless UX integration.
Experimentation & Evaluation: Skilled in rapid prototyping, benchmarking model outputs against product requirements, and setting up automated evaluation metrics.
Hands‑on experience with production observability and monitoring tooling to debug, trace, and measure live systems.
Experience using feature‑flag/experimentation platforms to safely roll out, A/B test, and decommission changes.
Familiarity with data persistence and async processing - relational databases and schema migrations, caching, and message/queue or task workflows.
A track record of writing well‑tested, maintainable production code with attention to code quality, security scanning, and dependency hygiene.
Familiarity with full‑stack development - building web UIs and backend services/REST APIs in Python.

Technical Leadership & Mentorship: Proven track record of technically leading engineering workstreams, taking ownership of the development lifecycle, and mentoring junior‑to‑mid level engineers.
Collaborative Communication: Excellent interpersonal skills, with a knack for bridging the gap between product management, design, and foundational ML infrastructure teams.
Thrives in Ambiguity: Highly autonomous builder capable of taking open‑ended product goals and breaking them down into concrete, scalable engineering realities.