Top Tech Jobs & Startup Jobs in Vancouver

Designs and operates cloud infrastructure, distributed systems, CI/CD pipelines, Kubernetes and EKS environments for a high-scale SaaS platform. Responsibilities include infrastructure automation, capacity planning, performance optimization, monitoring, alerting, security, infrastructure as code, developer productivity tooling, and maintaining highly available systems. The role collaborates closely with developers and platform engineers to deploy reliable, scalable services across AWS and GCP.

Build and operate scalable full-stack features for Evisort’s AI-driven contract management platform. Collaborate with engineering, data science, product, and design teams to deliver user-centric SaaS solutions. Own functional areas from development through production, apply strong engineering and DevOps practices, contribute to roadmap and architecture decisions, mentor teammates, and translate complex problems into clear solutions in an agile environment.

Lead the vision, strategy, roadmap, and execution for Workday Evisort’s Contract Intelligence and Analytics portfolio. Partner with engineering, data science, design, marketing, customers, and domain experts to build and launch generative and predictive AI products. Define requirements, evaluate market opportunities, develop monetization strategies, track product metrics, and deliver contract intelligence capabilities for enterprise customers.

Designs, develops, and deploys Workday integrations, data migrations, reports, and analytics solutions for clients. Leads integration workstreams across requirements gathering, solution design, development, testing, and production deployment. The role requires technical consulting expertise, financials or related domain experience, leadership across software implementations, advanced Workday integration skills, and bilingual English/French communication for Canadian clients.

Build and operate full-stack features powering AI agents within HR and Finance workflows. Own user-facing experiences and backend services through production, collaborate with machine learning engineers and product teams, apply distributed systems and cloud engineering practices, and mentor teammates. The role requires Python web development, relational databases, SaaS experience, containerization, DevOps, testing, and observability expertise.

Serve as the analytics and data science partner for Workday Data Cloud, connecting demand, sales pipeline, product usage, and consumption-based pricing. Investigate ambiguous business questions, derive decision-ready insights, create visualizations and executive narratives, and advise senior stakeholders. Build curated data assets, improve data connections and pipelines, address instrumentation gaps, and collaborate with Product, Engineering, and GTM teams to support strategic growth and data-driven decision-making.

Build and maintain front-end features for an AI-driven contract management SaaS. Collaborate with engineers, data scientists, product, and design; own functional areas from development to production; mentor teammates; apply modern dev practices to deliver scalable, user-centric solutions.

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.