2 days ago
Vancouver, CanadaSenior
Responsibilities
- Lead end-to-end system design, architectural framework development, and product integration for intelligent agents.
- Design and orchestrate complex agentic workflows, including multi-agent frameworks, routing layers, and advanced RAG pipelines.
- Integrate foundation models and modern AI APIs into user-facing enterprise products.
- Architect responsible and governed AI systems with privacy, security, predictability, explainability, and evaluation guardrails.
- Optimize latency, cost, token usage, context-window efficiency, reliability, and user interaction design.
- Lead cross-functional technical pods, mentor senior engineers, and guide product development from concept through deployment.
- Prototype, benchmark, and continuously evaluate agent behavior against product requirements.
Requirements
- At least 10 years of professional software engineering experience with 2+ years building production-grade LLM or agentic systems, or at least 7 years in Machine Learning Engineering or AI application development with 3+ years shipping LLM-backed products.
- At least 3 years of hands-on experience integrating LLMs, foundation models, and modern AI APIs into user-facing enterprise products.
- At least 2 years designing and scaling AI orchestration architectures, including multi-agent frameworks, routing layers, and advanced RAG pipelines.
- At least 6 years optimizing application performance, including at least 2 years addressing LLM-specific constraints such as token management, cost optimization, and context-window efficiency.
- At least 6 years leveraging cloud computing platforms such as AWS or GCP to deploy scalable, responsive systems.
- Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field; a Master’s degree is preferred.
- Deep understanding of responsible AI governance, guardrails, security layers, and evaluation mechanisms for autonomous agents handling sensitive enterprise data.
- Proven ability to technically lead cross-functional teams, mentor senior engineers, and steer product development through deployment.
- Expertise architecting robust application layers around AI models, including error handling, predictability, and seamless user-experience integration.
- Ability to rapidly prototype, benchmark model outputs, manage experimentation cycles, and turn ambiguous product goals into scalable engineering solutions.
Benefits
- Flexible hybrid work model requiring at least 50% of each quarter in the office or in the field with customers, prospects, and partners.
- Eligibility may include the Workday Bonus Plan or role-specific bonus and annual refresh stock grants.
- Workday provides comprehensive benefits and reasonable accommodations for qualified applicants.
Tech Stack
Categories
About Workday
Workday builds cloud-based enterprise applications for human capital management and financial management, including payroll, time tracking, expenses, planning, and procurement, sold on a subscription basis with professional services. Founded in 2005 and headquartered in Pleasanton, California, it is a public company traded on NASDAQ as WDAY. Organizations worldwide use Workday to unify HR and finance data, automate processes, and apply AI to workforce and financial operations.