Top AI Jobs in Vancouver

Design, develop, deploy, and optimize generative AI applications using LLMs and AI frameworks. Build scalable APIs, integrate foundation models and third-party platforms, automate business workflows, and connect AI solutions to internal systems and data sources. Monitor model quality, improve prompts and configurations, support responsible AI governance and security, evaluate emerging technologies, and collaborate with product, engineering, and business teams.

Designs, implements, and optimizes AI-powered workflow automation solutions. Responsibilities include analyzing business processes, documenting current and future-state workflows, identifying automation opportunities, gathering requirements, integrating automation with business systems, supporting testing and deployment, monitoring performance metrics, troubleshooting issues, training users, and promoting automation best practices and continuous improvement.

Leads the architecture, development, optimization, deployment, and lifecycle management of enterprise machine learning systems. Establishes AI engineering standards, MLOps practices, data quality processes, model governance, security, and validation frameworks. Provides technical leadership, mentors engineers, evaluates emerging technologies, and supports AI transformation initiatives while partnering with data scientists, engineering, product, and business teams.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct prompt experiments, improve response quality, maintain prompt libraries, document standards, and support governance. Collaborate with AI engineers, trainers, product teams, and business stakeholders to translate requirements into prompting strategies and support Generative AI implementations. Research emerging techniques, evaluate tools, and recommend improvements to AI quality and effectiveness.

Lead the organization’s AI workflow automation strategy, identify and prioritize automation opportunities, design scalable AI-powered workflows, and manage initiatives from concept through implementation. Partner with business and technical stakeholders, track budgets and timelines, establish governance, measure ROI and operational outcomes, develop performance dashboards, evaluate emerging technologies, and drive continuous improvement, adoption, and organizational transformation.

Labels, categorizes, validates, and quality-checks structured and unstructured data used to train AI and machine learning models. Maintains dataset accuracy, consistency, documentation, and version control while following detailed annotation guidelines. Collaborates with AI engineers, data scientists, and quality teams, identifies errors, provides process feedback, and supports continuous improvement of annotation workflows.

Reviews AI-generated content for accuracy, relevance, quality, safety, compliance, and adherence to organizational standards. Identifies factual inconsistencies, bias, harmful outputs, and recurring issues; documents findings and escalates concerns. Provides structured feedback to AI trainers and prompt engineers, tracks quality metrics, prepares reports, supports root-cause analysis, and helps improve prompts, training processes, and review guidelines.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering and evaluation strategies, and ensures secure, scalable production deployments. Partners with executives and business leaders to define AI roadmaps, mentors engineering teams, evaluates emerging technologies, and drives proof-of-concept initiatives and responsible AI practices.

Develop and manage the strategy, roadmap, lifecycle, and delivery of AI-powered products. Translate business needs into product requirements, collaborate with engineering and AI teams, define success metrics, monitor adoption and ROI, and drive continuous improvements. The role also engages customers and stakeholders, evaluates emerging technologies, supports governance and compliance, and promotes responsible AI practices.

Supports responsible AI governance by developing frameworks, conducting risk and impact assessments, evaluating fairness and transparency, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight processes. Collaborates with technical, legal, compliance, risk, and business teams to promote ethical, accountable, and trustworthy AI deployment.

Designs, tests, evaluates, and optimizes prompts for large language models and AI applications. Develops reusable prompt templates, conducts experimentation and A/B evaluations, monitors AI performance, and establishes quality standards. Collaborates with AI engineers, product managers, trainers, and business stakeholders to translate requirements into scalable prompting strategies. Researches generative AI advancements, supports model evaluation and tuning, documents best practices, and recommends improvements to response quality, consistency, accuracy, and business relevance.

Designs, tests, evaluates, and refines prompts for generative AI applications across customer support, content generation, workflow automation, knowledge management, and productivity use cases. The role evaluates AI outputs for accuracy, relevance, tone, and consistency; documents testing results; maintains prompt libraries and guidelines; collaborates with AI engineers, product teams, data scientists, and business stakeholders; and researches emerging AI tools and prompting techniques.

Manage and optimize infrastructure supporting AI and machine learning systems, including cloud environments, GPU/CPU clusters, model deployment, data pipelines, security, monitoring, troubleshooting, automation, and CI/CD. The role develops scripts, maintains documentation, supports AI teams, and improves system reliability and scalability. Candidates need a relevant bachelor's degree, at least two years of systems administration or infrastructure experience, cloud and containerization expertise, scripting skills, and familiarity with machine learning frameworks and deployment pipelines.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, developing business cases, gathering requirements, facilitating stakeholder workshops, supporting solution design and delivery, measuring benefits, and contributing to AI governance and continuous improvement.

Evaluates and validates AI chatbot and conversational AI performance through test plans, regression testing, user acceptance testing, and conversation analysis. Documents defects, verifies business requirements, identifies conversational gaps, reports findings, and recommends improvements. Collaborates with AI trainers, prompt engineers, and development teams to optimize chatbot quality, compliance, reliability, and user experience. Supports evaluation frameworks, testing standards, and continuous improvement initiatives.

Develop, deploy, and optimize scalable machine learning solutions, including predictive, deep learning, and intelligent automation models. Build data pipelines, prepare large datasets, monitor production models, manage retraining, and ensure quality, security, compliance, and responsible AI practices. Collaborate with data scientists, engineers, product teams, and stakeholders while researching emerging AI technologies and improving AI architectures.

Oversee and optimize infrastructure supporting AI and machine learning platforms, including servers, cloud environments, compute resources, GPU/CPU allocation, security controls, monitoring, and incident response. Design scalable AI environments, automate operational processes, troubleshoot system issues, and support model deployment and training workloads. Mentor junior administrators, evaluate emerging technologies, and recommend improvements to AI platform architecture, reliability, security, and scalability.

Build and optimize production-grade agentic AI systems for marketing and enterprise customer experience use cases. Responsibilities include agent orchestration, tool use, RAG, embeddings, guardrails, LLM evaluation, observability, performance optimization, and scalable Python services deployed on AWS. The role partners across product and engineering teams and requires hands-on experience with LLM agents, marketing data platforms, retrieval systems, and AI evaluation.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Leads architecture for Fortive’s HR technology ecosystem, including Oracle HCM, Boomi, ServiceNow HRSD, Eightfold, LMS platforms, and enterprise integrations. Defines API-first integration strategies, data governance, security standards, and modernization roadmaps. Designs and deploys AI, automation, NLP, and chatbot capabilities for HR workflows. Advises HR and IT stakeholders, guides vendors and technical teams, and mentors integration developers while ensuring compliance, data quality, and scalable platform architecture.

Own the technical sales motion for NVIDIA-focused enterprise AI infrastructure deals. Lead discovery, architecture, proof-of-concept design and execution, technical closes, partner and field SE enablement, troubleshooting, escalation, customer workshops, and product feedback. The role requires expertise in NVIDIA systems, storage, data platforms, Linux, parallel file environments, GPU clusters, and AI pipelines, with Bay Area availability for on-site partner or customer sessions.

Own technical SEO, answer-engine optimization, AI/LLM visibility, and organic social media for a B2B technology brand. Develop search and topic strategies, measure visibility across AI assistants, manage social channels and campaigns, identify content gaps, and improve organic engagement, form fills, MQLs, opportunities, and revenue attribution. Collaborate with content, product marketing, communications, and operations teams while continuously testing and optimizing performance.