Top Tech Jobs & Startup Jobs in Vancouver

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.

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.

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.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

As a Senior Backend Software Engineer, you'll enhance the AI-driven contract management platform by collaborating with teams, optimizing code, and ensuring platform performance through API development and monitoring strategies.

As a Software Engineer on the Pipedream team, you'll enhance Pipedream Connect's capabilities, develop APIs, and work with a diverse tech stack, including Go and Node.js, to deliver impactful integrations for customers.

Design and build core ML systems for production-grade AI agents, owning model, agent logic, orchestration, deployment, monitoring, and continuous improvement. Implement RAG pipelines, workflow orchestration, evaluation and feedback loops, and ensure scalable, observable, explainable, enterprise-ready solutions in collaboration with product, software, and data teams.

Design and build production-grade ML systems and LLM-powered agent frameworks (RAG, orchestration, evaluation). Own model lifecycle from data strategy to deployment, monitoring, and continuous improvement, partnering with software engineers, product managers, and data scientists to integrate scalable, observable, and enterprise-ready agents.

Design, build, and deploy scalable AI features using LLMs, knowledge graphs, and orchestration frameworks. Integrate models into enterprise products, optimize performance and latency, and manage model lifecycle, observability, and cloud-native deployments. Collaborate cross-functionally, lead engineering workstreams, and maintain high quality, secure production services.