Top Tech Jobs & Startup Jobs in Vancouver

Lead technical direction and implementation of a large-scale AI model serving platform. Design, build, and operate distributed systems (Ray/Ray Serve), ensure scalability, reliability, monitoring (Prometheus), CI pipeline robustness, and evolve the production model registry while collaborating with ML engineers and stakeholders.

Lead development of scalable AI platforms and production machine learning services. Design agentic AI capabilities including tool calling, multi-agent systems, planning, reasoning, and human-in-the-loop workflows. Build Python services and Kubernetes-based ML infrastructure, multi-tenant runtimes, data pipelines, and cloud deployments. Apply MLOps, automation, observability, CI/CD, testing, and distributed-systems practices while collaborating with engineers, researchers, and cross-functional teams.

Lead the design, development, scaling, and operation of Workday’s AI model-serving platform. Build distributed systems for traditional machine learning models and large language models, optimize Kubernetes and GPU infrastructure, improve reliability and observability, troubleshoot production issues, maintain CI pipelines, review code, write technical documentation, and mentor engineers while driving architectural decisions.

Own AI agent evaluation frameworks and deployment across Core Financials. Lead benchmark development, customer and ecosystem discovery, API and data-service integrations, cross-agent standards, and scalable self-service product frameworks. Partner with engineering, UX, research, finance leaders, and audit firms to translate requirements into production capabilities. Manage feature delivery, resolve technical dependencies, align stakeholders, communicate results to executives, and support monetization of Workday’s Financials AI portfolio.

Serve as the Finance-focused functional expert enabling GSI and ecosystem partners on Workday Financials and agentic AI. Develop playbooks, translate roadmap into outcomes, support partner positioning, demos, competitive messaging, and regulatory/risk considerations for finance AI use cases. Collaborate with solution architecture, solution consulting, and partner teams to ensure partner readiness, demo fidelity, and go-to-market success.

The role involves architecting AI solutions, optimizing ML systems, and deploying advanced retrieval frameworks. Engineers will lead ML lifecycle from research to production, collaborating across teams to build AI-first products.

Enable strategic GSI and ecosystem partners on Workday HCM and agentic AI: develop functional playbooks, translate roadmap into partner-ready narratives, advise on competitive positioning and regulatory risks, support demo readiness, and act as a first-line functional expert for partner HCM teams.

Develop and deploy production machine learning products for financial workflow automation. Responsibilities include designing ML features, training models, building APIs and scalable services, evaluating model performance, ensuring observability, and applying LLMs, RAG, natural language understanding, and autonomous agents to business documents and workflows. The role also requires technical leadership and mentoring junior engineers.

Lead design and delivery of production-grade agentic AI systems: architect LLM integration, orchestration, and routing; ensure reliability, low latency, cost efficiency, and Responsible AI guardrails; translate AI capabilities into enterprise product value and mentor cross-functional teams.

As a Principal Machine Learning Engineer, you'll design and implement core ML systems for AI agents, collaborating with cross-functional teams and overseeing the full lifecycle of products from data strategy to deployment.