Top AI Jobs in Vancouver

Lead finance analytics, own data warehouse and BI strategy, build Finance AI automation and model foundation, optimize GCP costs, create self-service analytics, ensure data governance and audit readiness, and manage a cross-functional team supporting FP&A and accounting.

Lead AI-enabled marketing operations to scale global campaigns, optimize demand generation, manage marketing tools and processes, and collaborate with events, digital, and communications teams. Operate as a full-time international contractor supporting WeTravel's mission to grow group travel operator adoption worldwide.

Design and build AI-powered tools and UI frameworks for Unreal Engine. Own and evolve UI tooling codebase, profile and fix performance/memory issues, mentor engineers, and collaborate across teams to improve developer workflows and support licensees.

Design, build, and own production ML and LLM-driven features (RAG, prompt engineering, fine-tuning, semantic search). Write maintainable code, create design docs, troubleshoot issues, collaborate with product/design, and participate in Agile development. Work with Python/Java/JavaScript, ML frameworks, and cloud/container technologies.

The AI Engineer will design, build, and deploy AI-powered solutions for clients, diagnose operational inefficiencies, and develop automated workflows, requiring hands-on engagement and communication skills with stakeholders.

This role involves optimizing AI systems for large model training and inference, focusing on performance, efficiency, and usability using low-precision training and reinforcement learning techniques.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

This role involves driving new client acquisition and revenue growth through full-cycle sales execution and relationship building with stakeholders, while maintaining pipeline tracking in CRM.

Senior individual contributor who architects, prototypes, and ships production-grade AI solutions for Finance. Focus areas include agentic AI, multi-agent orchestration, ML forecasting and anomaly detection, RAG and vector-store solutions, responsible AI and governance, and embedding AI into FP&A processes. Partners with engineering, IT, security, and finance teams to productionize systems and establish standards, monitoring, and controls.

Lead and deliver an enterprise AI platform and agent factory: design architecture, build cost-effective model infrastructure, harden security/SSO/IAM, enable developer adoption, and build agent workflows across the PDLC (ideation, development, deployment). Mentor engineers, steer delivery partners, and remain hands-on implementing agents, RAG pipelines, integrations, and observability to drive measurable productivity gains.

Lead AI-first marketing strategy and execution: design, prototype, validate, and deploy production AI workflows. Own and evolve the marketing data stack, reporting, and analytics. Manage two analysts, transition consultant work in-house, and partner with marketing leadership to drive data-driven insights and upskill the organization.

Design, build, and operate production-grade generative and agentic AI applications and services. Develop Python backend services, RAG pipelines, multi-agent systems, LLM integrations, APIs, and cloud deployments. Implement observability, security, and engineering best practices while mentoring junior engineers and driving platform architecture decisions from prototype to production.

Design, build, and operate production-grade Generative and Agentic AI applications and services. Develop Python backend services, integrate cloud-hosted LLM APIs, implement RAG pipelines and vector search, build multi-agent systems, deploy on major cloud providers, ensure observability, security, and performance, and mentor junior engineers.

Design, develop, and optimize high-performance Java backend systems. Contribute to architecture, implement core modules, troubleshoot production issues, perform code reviews, and improve scalability, availability, and reliability of distributed financial or blockchain systems.

Lead architecture, vendor strategy, and delivery of enterprise-grade agentic AI systems. Build production multi-agent workflows, control planes, and compliance posture. Act as senior technical client interlocutor, hire and mentor engineers, and create reusable engineering patterns and observability for scalable, auditable enterprise deployments.

Design, build, and maintain production-ready full-stack applications combining React frontends with Java/Python backends. Integrate Generative and Agentic AI workflows, implement CI/CD, deploy cloud-native services on Kubernetes (GCP/Azure), manage relational and NoSQL databases, apply automated testing, observability, and troubleshoot production systems while collaborating across DevOps, QA, and Product teams.

Design, build, and own production ML and LLM-powered product features (chat, RAG, semantic search, data extraction). Write maintainable code and docs, debug issues, collaborate with product and design, follow Agile practices, and stay current on AI/ML methods.

The role involves deep troubleshooting, root cause analysis, and architectural optimization in the Data and AI ecosystem to enhance platform reliability and supportability.

Lead development of foundation and multimodal models for whole-body MRI, design and validate experiments, drive data quality and annotation standards, apply generative/agentic AI and LLM adaptation, collaborate with clinical and engineering teams, and deliver production-ready, clinically signed models with documented validation and performance benchmarks.

Design and deploy practical AI automations and agents using low-code/no-code tools. Identify high-impact automation opportunities, build and troubleshoot workflows, implement RAG and prompt engineering, measure ROI, and support governance and data-risk practices across teams.

Conduct AI/ML literature and technology trend research, produce technology reports, evaluate and recommend cutting-edge AI/ML methods, collaborate with cross-functional teams to plan and implement AI projects, and promote innovation and continuous improvement within the AI/ML team.

Design, prototype, and ship production AI/ML features that analyze observability data to detect, triage, and resolve incidents. Rapidly iterate on LLM- and agent-powered workflows, collaborate cross-functionally, integrate AI with internal tools and alerting systems, and ensure solutions are scalable, maintainable, and aligned with user workflows.

The Partner Manager will drive revenue growth through strategic partnerships, managing partner relationships, and collaborating with sales and marketing teams.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.