Top AI Jobs in Vancouver

The AI Product Designer will shape user experience by leading the design of AI-native systems, simplifying tasks, and guiding the design team in collaboration with product and engineering.

Lead the product strategy and roadmap for a cloud-native, AI-driven security operations platform that unifies video, access control, and communications. Own full product lifecycle: customer research, requirements, prioritization, and cross-functional delivery with engineering, AI, and UX teams to ship agentic AI features that automate incident response and simplify B2B SOC workflows.

Lead end-to-end development of LLM and agentic AI systems for clinical applications: fine-tuning, domain adaptation, multimodal integration with imaging and structured data, rigorous evaluation and hallucination mitigation, production deployment with clinical validation, and cross-functional technical leadership to establish org-wide frameworks and standards.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Research and optimize AI and machine-learning systems across desktop and mobile platforms. Evaluate model performance, develop and deploy on-device applications and agents, and improve browser ML products. Apply systems and network expertise, collaborate with privacy and security researchers, and investigate AI risks. The role involves open-source development, performance benchmarking, and use of modern ML frameworks to deliver privacy-focused solutions.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Industry Expert who reviews and defends advanced AI, ML, distributed systems, and forward-deployed engineering projects. Responsibilities include challenging architecture decisions, chairing mock review boards and executive presentations, hosting one or two technical sessions monthly, and serving as the escalation point for instructors. The role requires production-scale engineering judgment, strong technical communication, and expertise in agentic systems, LLM evaluation, infrastructure, observability, model serving, RAG, or enterprise deployments.

Manage and grow full stack engineering teams focused on data visualization and business semantics, overseeing recruitment and development of talent, implementing strategies, managing technical debt, and ensuring high-quality software delivery.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Design, build, and deploy production-grade AI applications, integrations, pipelines, and agentic workflows for customers. Lead discovery, prototyping, architecture workshops, technical troubleshooting, and customer go-lives. Integrate LLMs, retrieval systems, tools, and orchestration into enterprise environments, establish reusable engineering patterns, mentor customer-facing engineers, and translate customer feedback into product and engineering priorities.

Lead product discovery and delivery for client engagements, partnering with stakeholders, designers, and engineers to define opportunities, prioritize backlogs, manage roadmaps, and deliver measurable outcomes. Serve as a trusted advisor to senior clients while guiding AI-enabled product development. Apply AI tools for productivity and demonstrate practical understanding of LLM systems, RAG, data grounding, hallucinations, latency, cost, and evaluation.

Execute AI-powered outbound prospecting: design and optimize automated workflows, run multi-channel outreach, qualify leads, book meetings, experiment to improve conversion, and consistently hit monthly targets.

Execute AI-powered demand generation using LLMs and multi-agent workflows to scale content, ABM outreach, AEO/SEO optimization, and analytics-driven campaign measurement across website and product channels.

Build and integrate production-grade AI agents using LLMs to automate security operations. Implement orchestration, tool/function calling, guardrails, testing, observability, and human-in-the-loop controls while working across backend and web stacks to deploy reliable, auditable agentic features.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Own multiple client product engagements from discovery through delivery, including AI agent development, greenfield product builds, legacy platform modernization, and AI-native enablement. Translate ambiguous needs into PRDs, roadmaps, success criteria, and sequenced work; partner with engineers on agent architecture, evaluations, and guardrails; manage stakeholders, scope, budgets, timelines, and ROI. Lead client presentations, workshops, steering committees, and internal delivery playbooks.

Join Fortinets FortiGuard IoC team to build and deploy ML models and AI tools for threat detection, adversarial ML defenses, and automation. Work with data scientists, DevOps, and SOC analysts to integrate LLMs, wrap models as APIs, develop agentic workflows, and produce IoCs to improve detection and response.

Create, publish, and maintain technical documentation for Fortinet cybersecurity products. Use GenAI/LLMs and scripting to automate authoring, release notes, and publishing. Collaborate with product, engineering, QA, and stakeholders to translate complex features into user guides, references, and use-case scenarios. Analyze knowledge gaps with data analytics and train LLMs to support customer queries.

Design and execute test plans for AI features and surrounding application logic, perform data verification for training/validation/production datasets, set up and maintain automated tests, identify and track software defects, and review AI feature requirements to support FortiOS testing.

Lead design and implementation of Microsoft Fabric solutions, build data pipelines, enforce data governance, integrate Fabric with cloud/on-prem systems, mentor engineers, optimize storage and reporting, and manage data asset lifecycle.