About the Role As a Senior AI Engineer , your primary focus will be building AI features and intelligent agents specifically for the Contract Lifecycle Management (CLM) domain, though you will also be expected to contribute to other areas of the product as needed.

While AI development is the primary deliverable, this is first and foremost a software engineering role requiring deep expertise in backend architecture and distributed systems .

You will be responsible for architecting advanced LLM-driven solutions and agentic workflows that are not only innovative but also enterprise-grade—meaning they are highly available, scalable, and secure.

By leveraging Workday's rich datasets and computing resources, you will build the robust infrastructure necessary to deliver transformative AI value to our customers at scale.

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.

Operational Excellence A key component of this role is the continuous evaluation and improvement of the customer experience with AI agents.

You will be required to rigorously assess agents' performance and user interactions to ensure our intelligent solutions are meeting user’s needs.

Furthermore, you will actively work to integrate these agents into the broader Workday agents ecosystem, ensuring seamless interoperability and consistent AI value across the entire Workday platform.

About You Basic Qualifications 8+ years of qualified 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 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 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 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 benefits, please .

Primary Location: CAN.BC.

Vancouver

Primary Location Base Pay Range: $169,000 CAD - $253,000 CADPrimary CAN Base Pay Range: $169,000 - $253,000 CAD

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