Top AI Jobs in Vancouver

Build and deploy AI/LLM-powered features and RAG pipelines for internet-scale threat intelligence. Prototype, fine-tune, evaluate, and optimize models; integrate AI into platform services; implement CI/CD, regression testing, and security guardrails; collaborate across product, frontend, and data teams to deliver real-time, low-latency AI-driven analytics and automation.

Administer and configure conversational AI, agent assist, workflow automation, and related platforms. Manage users, permissions, environments, releases, integrations (CRM, contact center, auth, knowledge), deployments, testing, and governance. Troubleshoot APIs/integrations, maintain documentation and change history, and support security, privacy, and compliance while working with analytics to optimize AI workflows.

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.

Lead enterprise AI projects to deliver high-quality solutions on time and budget, adapting project management methodologies and driving business change management.

Own client operating rhythm for Creative Operations and Systems/Process Design clients: maintain stakeholder context, turn vague requests into clear actions, use AI-enabled workflows for prep and follow-up, produce client-ready summaries, maintain decision logs and trackers, and coordinate with internal teams to prevent scope drift and improve account knowledge reuse.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Build and operate internal AI platform services: chat/agent frontends, retrieval over code/docs, AI-native developer workflows, sandboxed execution, model routing, telemetry, and compliance-minded design for regulated aerospace environments.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Lead design and implementation of LLM-powered product features, integrate AI into Vanta systems, instrument evaluations and monitoring, collaborate with platform and product teams, make pragmatic technical tradeoffs, mentor engineers, and drive AI product quality and adoption to help customers accelerate compliance and risk management.

The Principal Engineer will architect and own the Shop Builder platform, defining APIs, developing tools for developers and AI agents, and ensuring the platform's technical standards and documentation are met.

Lead a team to deliver tools and API solutions for the Shop Builder platform. Coordinate cross-team dependencies and maintain engineering standards while actively contributing code and driving technical decisions.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.

Conduct research and engineering on VFX and procedural shape/texture/VFX generation, develop AI/CG prototypes, transfer solutions into Huawei products, and publish findings as papers or patents.

The engineer will develop features for an AI workspace platform, deploy AI solutions in public sector environments, and interact with clients.

Lead client relationships for major entertainment brands, translate creative vision into briefs and social campaigns, use AI-first tools and agentic workflows to plan, coordinate, review, and improve work. Protect quality, surface risks and opportunities, and encode reusable workflows. Drive account growth through strategic judgment, clear communication, and operational excellence.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

The Principal AI Solutions Consultant will develop AI-powered automations and MVP solutions, delivering production-ready work within 6-8 weeks, focusing on healthcare operations.

This role involves leading enterprise clients in enhancing their Microsoft 365 collaboration using AI technologies, while also ensuring security and compliance. The consultant will architect, implement, and govern solutions such as SharePoint Online and manage AI integration.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.