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.

In addition to contributing to feature and service development, you must have an approach of continuous improvement, passion for quality, scale, and security.

You must be curious and prepared to question or challenge choices and practices where they don't make sense to you or could be improved.

You also should have a product approach and strong intuition around how AI can drive a better customer experience.

Lastly, a strong sense of ownership and teamwork are essential to succeed in this role.

About You Basic Qualifications 8+ years of professional software engineering experience, with a heavy emphasis on backend architecture, distributed systems, and a proven track record of shipping high-availability production code at scale.

Strong fundamental software engineering skills (system design, scalability, and performance optimization) are a non-negotiable primary qualification for this role. 3+ years of experience integrating AI capabilities (LLMs, Foundation Models) into enterprise products, framed within the context of robust software service extensions. 1-2+ years building with AI orchestration frameworks (e.g., Lang

Chain, Llama

Index) with a focus on creating predictable, reliable, and maintainable agentic workflows. 4+ years of proven experience leveraging cloud computing platforms (e.g., AWS, GCP, Azure)



to deploy highly responsive, scalable systems. 2+ years of hands-on experience deploying and operating services in containerized, cloud-native environments (Docker, Kubernetes), with exposure to infrastructure-as-code and Git

Ops 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 (e.g., assessing retrieval quality and agentic behavior).

Hands-on experience with production observability and monitoring tooling (e.g., Prometheus/Grafana, Open

Telemetry, Sentry, Datadog) to debug, trace, and measure live systems Experience using feature-flag/experimentation platforms (e.g., Launch

Darkly) to safely roll out, A/B test, and decommission changes, including flag lifecycle and cleanup discipline Familiarity with data persistence and async processing - relational databases and schema migrations (e.g., PostgreSQL), caching (e.g., Redis),



and message/queue or task workflows (e.g., Celery, Kafka, SQS) A track record of writing well-tested, maintainable production code with attention to code quality, security scanning, and dependency hygiene (e.g., unit/integration tests, Sonar

Qube, dependency tooling) Familiarity with full-stack development - building web UIs (e.g., React, Type

Script) and backend services/REST APIs in Python (e.g., FastAPI, Django) 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.

Workday Pay Transparency Statement The annualized base salary ranges for the primary location and any additional locations are listed below.

Workday pay ranges vary based on work location.

As a part of the total compensation package, this role may be eligible for the Workday Bonus Plan or a role-specific commission/bonus, as well as annual refresh stock grants.

Recruiters can share more detail during the hiring process.

Each candidate’s compensation offer will be based on multiple factors including, but not limited to, geography, experience, skills, job duties, and business need, among other things.

For more information regarding Workday’s comprehensive perks, please .

Primary Location: CAN.BC.

Vancouver

Primary CAN Base Pay Range: $169,000 - $253,000 CADAdditional CAN Location(s) Base Pay Range: $169,000 - $253,000 CAD

📌 Senior AI Engineer - Evisort AI (Vancouver)
🏢 Workday
📍 Vancouver