About the Team

Agent Factory is where Workday's next chapter gets built. Within our AI organization, small, senior, cross-functional pods bring together product leaders, machine learning engineers, UX designers, and full-stack builders to create intelligent agents used by millions of people every day.
This is production-grade AI embedded deeply into Workday's platform — not research experimentation or maintenance work. We ship to paying enterprise customers today, and we're kicking off a new AI agent initiative that will define how our products reason and act on behalf of users. High trust, high expectations, and real impact. Engineering, but brighter.

About the Role

You'll own the end-to-end design, implementation, and product integration of our intelligent agents — and build the full-stack product surface those agents live in. Our ML Engineers build and optimize the foundational algorithms; your mission is intelligence orchestration and product delivery, connecting the brain to the product.

Expect to be hands-on across React frontends, backend microservices, APIs, data models, asynchronous workflows, agent orchestration layers, and delivery infrastructure. Because these agents work with sensitive HR and financial data at global scale, you'll help implement the guardrails that keep them private, predictable, and explainable.

This is the most senior engineering role on the team. You'll set the technical direction for our agent architecture, mentor the engineers around you, and be the person who presents the work when the stakes and visibility are highest.

• Design and implement agentic workflows — orchestration and routing layers, tool calling, retrieval pipelines, memory, and multi-step reasoning — and integrate foundation models into customer-facing HR and Finance workflows.

• Build and operate end-to-end features across React frontends, backend microservices, APIs, data models, and CI/CD. You build it, you run it.

• Own the constraints that make agents viable in production: latency, cost per interaction, context-window efficiency, determinism, and graceful failure.

• Build the evaluation loop — offline and online evals, benchmarking agent behavior against product requirements, and production observability of agent traces.

• Implement Responsible AI in practice: guardrails, permission and data-boundary enforcement, PII handling, auditability, and explainability.

• Lead high-visibility projects end to end and present them with confidence in design reviews, demos, and executive updates.

• Make the call when the team needs one — gather input quickly, commit to a direction, document the trade-offs, and adjust as evidence arrives.

• Mentor engineers, raise the technical bar, and partner closely with Product, UX, Machine Learning, and platform teams.

About You

Basic Qualifications

• 10+ years of professional software development experience, including significant full-stack ownership from modern frontend through backend services.

• 5+ years with backend web frameworks; Python frameworks such as FastAPI, Flask, or Django preferred.

• 2+ years integrating large models (LLMs, foundation models) and modern AI APIs into production, user-facing products.

• 1+ years designing and scaling AI orchestration architectures — multi-agent or tool-calling frameworks, routing layers, or advanced RAG pipelines.

• 4+ years using cloud platforms (e.g., AWS, GCP) to deploy responsive, scalable systems.

Other Qualifications

• Track record of shipping AI and agentic features that real customers use in production, and owning them after launch — prototypes alone aren't the bar.

• Product-first mindset: you apply large models to solve practical user problems, not to showcase the technology.

• Practical command of LLM constraints (token and cost management, context-window efficiency, caching, streaming, fallbacks) and of evaluation — rapid prototyping, benchmarking outputs, and automated metrics for retrieval quality and agentic behavior.

• Strong understanding of the governance, guardrails, and security layers required when deploying autonomous agents over sensitive enterprise data.

• Proven ability to architect the application layer around AI models: reusable patterns for predictability, error handling, and seamless UX integration.

• Strong experience with React or a comparable framework, TypeScript, state management, and modern build tooling, plus a commitment to accessibility (WCAG 2.1/2.2).

• Deep knowledge of backend service architecture, microservice patterns, and REST API design, with proficiency in Python and ideally Kotlin or Java.

• 5+ years with relational databases (PostgreSQL preferred) and non-relational stores, including query analysis and indexing optimization.

• Strong grasp of distributed-system trade-offs, with the ability to independently diagnose and optimize performance at scale.

• Hands-on with Infrastructure-as-Code, CI/CD automation, Docker and Kubernetes, observability, incident support, and secure coding practices.

• A natural technical leader: people follow your direction because of your clarity and judgment, not your title. You measure success by what the team ships, not only by what you ship.

• Proven track record leading engineering workstreams end to end and mentoring junior-to-mid-level engineers into stronger ones.

• Confident presenter who is comfortable owning the room, and steady when a project is visible, ambiguous, or under real time pressure.

• Collaborative and decisive: you seek input broadly, disagree and commit, and keep momentum without steamrolling.

• Highly autonomous builder who turns open-ended product goals into concrete, scalable engineering plans.

• Bachelor's degree (Master's preferred) in Computer Science, Software Engineering, or an equivalent technical field.

• Experience integrating ML services or data pipelines into web applications; familiarity with Elasticsearch, rich-text editors or document-parsing tools, C4 diagramming, or Workday XO lifecycle management.

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 benefits, please .

Primary Location: CAN.BC.Vancouver

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

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

Our Approach to Flexible Work

With Flex Work, we’re combining the best of both worlds: in-person time and remote. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. We know that flexibility can take shape in many ways, so rather than a number of required days in-office each week, we simply spend at least half (50%) of our time each quarter in the office or in the field with our customers, prospects, and partners (depending on role). This means you'll have the freedom to create a flexible schedule that caters to your business, team, and personal needs, while being intentional to make the most of time spent together. Those in our remote "home office" roles also have the opportunity to come together in our offices for important moments that matter.