Top AI Jobs in Vancouver

Lead end-to-end feature development and modernization across diverse SaaS stacks, using AI-augmented tooling to accelerate delivery. Maintain legacy production stability, establish testing and secure deployment workflows, and mentor junior engineers while partnering with business stakeholders.

Configure and maintain legal technology platforms, build integrations and automated workflows, develop AI-assisted tools and dashboards, analyze operational data, and drive process improvements across the legal function while collaborating with IT, Product, and AI teams.

The Director of Software Engineering role focuses on enhancing Jobber's AI capabilities, overseeing the integration of intelligent workflows across products to support small business operations more effectively.

As a Senior AI Kernel Engineer, you will design and optimize high-performance kernels for AI inference on GPUs and custom accelerators, improving performance and collaborating with various teams to influence architecture and implementation.

The Technical Product Manager will lead product direction, interface with engineering teams, and ensure the successful deployment of AI-powered safety systems. Responsibilities include defining product vision, translating customer needs, and maintaining high product quality.

Define enterprise AI strategy and architecture for LLMs, RAG, and predictive models; design secure, high-performance REST/GraphQL APIs and event-driven systems; establish AI/data governance; and lead cross-functional teams to productionize real-time AI applications.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Design, build, and optimize scalable AI-enabled application features and services. Integrate ML models and generative AI, ensure secure and test-driven implementations, troubleshoot AI performance, mentor engineers, and drive AI architecture, CI/CD, and best practices across teams.

Build and operate large-scale, high-performance infrastructure to run AI inference and agents on real-time streaming data. Design scalable, reliable serving layers, solve distributed-systems challenges, and optimize across cloud providers.

Design and implement end-to-end AI systems and production-grade components. Build and maintain RAG pipelines, backend services, APIs, and data pipelines. Write Python code using Azure AI SDK. Implement document digitization, vectorization, semantic search, and content summarization. Prepare training/evaluation datasets, participate in code reviews, and drive engineering quality and deployment of AI-powered SaaS workflows.

Founding GTM rep responsible for building and qualifying pipeline across enterprises and AI startups, managing inbound requests and outreach automations, re-engaging sign-ups, coordinating discovery calls with founders/CRO, tracking CRM and pipeline metrics, attending events, and proposing GTM experiments and partnerships.

The Account Executive will manage the full sales cycle for enterprise and scale-up accounts, building relationships with technical buyers and collaborating closely with senior leadership to drive revenue and shape sales strategies.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

The Software Development Manager will lead the team in technical solutions, coaching team members, and creating roadmaps while contributing to code and reviews.

Lead and grow a software engineering team building enterprise contract-management AI products. Partner with product, ML, and design to deliver scalable SaaS solutions, own platform performance and operations, recruit and mentor engineers, and drive AI initiatives (LLMs, agents, RAG) with strong devops, testing, and observability practices.

Produce high-volume, on-brand, platform-native video using AI tools and Adobe (Premiere Pro, After Effects). Take briefs through creative and production phases for enterprise accounts, add captions/motion/sound, manage multiple projects, and feed learnings back into the Flywheel system.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Lead enterprise product vision and multi-year strategy for integrated product strategy and AI across ABC's portfolio. Build and manage a high-performing product and UX team, define AI-enabled platform and API-first capabilities, partner cross-functionally on go-to-market and technical decisions, and drive platform convergence, monetization, and measurable business outcomes.

Lead global strategic partnerships across cloud, GPU, and hardware providers. Identify and negotiate commercial agreements, manage key partner relationships, align cross-functional stakeholders, build business cases, and support executive strategy to enable Higgsfield's AI infrastructure scale and growth.

The AI/ML Engineer will design and develop AI-powered applications, automate workflows, and integrate AI into BPM's daily operations, ensuring they deliver business value.

Own and scale the Nx-based monorepo, build system, and CI/CD to make builds, tests, and developer workflows fast, reliable, and observable. Build developer tooling, local dev stacks, and production-grade developer infra with SLOs, on-call, and measurable DX improvements.

Lead end-to-end delivery of AI deployments for financial services clients: define features, translate business needs into technical specs, run agile sprints, drive adoption and change management, ensure measurable program outcomes, and expand commercial relationships while collaborating with engineers, product, design, and investment teams.

Design and implement advanced generative AI models, optimize deployment in production, collaborate with teams, and stay updated on AI advancements.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.