Top AI Jobs in Toronto

Support research and deployment of next-generation networking solutions for Huawei's global AI and cloud infrastructure. Analyze industry trends to identify technical challenges and align research with business needs. Work on RDMA, network stack optimization, collective communication, congestion control, traffic engineering, topology and resource allocation, and collaborate with academia and industry to deliver infrastructure innovations.

Drive day-to-day delivery for AI initiatives: break work into tasks, run agile/sprint cadence, track blockers and dependencies in ClickUp, report weekly to stakeholders, coordinate vendors, maintain documentation and dashboards, and build lightweight automations to improve processes.

Hands-on multidisciplinary designer who concepts, prototypes, and produces high-quality visual and motion work using traditional tools and AI. Create campaigns, style frames, motion tests, social assets, storyboards, prototypes, and presentations while collaborating with internal and external teams and iterating quickly with attention to craft, brand fit, and responsible AI use.

Design, build, and deploy machine learning and deep learning models; integrate AI into customer-facing applications; run experiments, preprocess data, and maintain training/evaluation pipelines; optimize model performance and scalability; stay current with research and contribute to system design and code reviews.

Hands-on multidisciplinary designer who uses traditional design, motion, and AI tools to concept, prototype, and produce high-quality visual assets (social, video, campaigns, motion tests, prototypes). Collaborate with cross-functional teams, iterate rapidly, and apply prompt-writing and AI workflows to elevate craft, storytelling, and brand consistency.

Lead integration of workforce management strategy into enterprise AI and transformation initiatives across North American Customer Operations. Translate enterprise decisions into actionable workforce plans, assess capacity and financial impacts, establish governance, partner with Finance/Technology/HR, and support AWS and agentic AI adoption and operational readiness.

Responsible for bridging enterprise data with AI capabilities, translating business challenges into operational solutions, and driving complex sales deals by connecting technical solutions to business value.

Design, build, and deploy Generative AI solutions (LLMs, RAG, vector DBs, agents) for financial use cases. Architect scalable AI infra, containerize models, implement CI/CD and MLOps, evaluate model performance, and collaborate with engineering and stakeholders to productionize safe, reliable GenAI systems.

Lead product vision, roadmap, and delivery for AI and analytics across the Intelex platform. Conduct customer discovery, define PRDs and success metrics, partner with engineering and design, manage end-to-end launches, instrument and monitor feature adoption and quality, enable go-to-market teams, and ensure AI governance, explainability, and compliance for enterprise EHSQ use cases.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

Lead the AI & Innovation Solutions practice to translate business priorities into AI, Agentic AI, and automation initiatives. Define strategy and governance, assess and incubate use cases and proofs-of-concept, oversee Power Platform solutions, coordinate enterprise partners (Security, Privacy, Legal, Risk), manage vendors, drive adoption and change management, and mentor the team to deliver measurable business value and operationalized AI solutions.

Lead enterprise AI architecture: define strategy, target-state architectures, standards, roadmaps, and governance. Align AI investments, reduce duplication, ensure regulatory and risk compliance, and drive reusable platforms. Advise executives, chair governance, mentor architecture teams, and measure business value from AI initiatives while enabling responsible, scalable deployment across the organization.

Lead and support enterprise AI and analytics platform reliability, security, and scalability. Develop and maintain Amazon Bedrock capabilities and agentic workflows, apply DevOps and IaC practices, productionize services, run POCs and KTLO, and provide training and cross-team platform support.

The Sr. Machine Learning Engineer will design and deploy scalable ML systems, collaborate across teams, and ensure production readiness of ML models using modern frameworks and cloud services.

Lead execution of an enterprise AI program: govern a portfolio of cross-functional AI initiatives, build org-wide AI enablement and infrastructure, coordinate stakeholders and vendors, report program health to C-suite, and coach departments through AI adoption.

Design, build, and deploy AI/ML solutions including data pipelines, model fine-tuning, and production deployment. Develop real-time model-serving microservices, implement monitoring for drift and performance, collaborate with data and DevOps teams, and prototype LLM and generative AI solutions using structured and unstructured data.

The Co-op Engineer will analyze source code, implement new features on demo apps for Android or HarmonyOS, and deploy AI models on Huawei devices, collaborating across teams.

The Senior Forward Deployed Engineer will deliver technical solutions using AI, engage with customers, and lead project lifecycles while mentoring junior engineers.

Manage the vision, strategy, and roadmap for Contract Intelligence AI products. Collaborate with teams to define requirements and market strategies, ensuring delivery of generative AI solutions.

Design, build, and deploy AI-powered planning solutions on Pigment. Translate business problems into scalable models and custom AI agents, integrate data via APIs and pipelines, prototype agentic workflows, train users, drive adoption, and relay customer feedback to Product and Engineering to scale repeatable deployment patterns.

Lead end-to-end AI product development: define KPIs, design and train foundation/transformer models, run rigorous experiments (A/B, causal inference), build production training pipelines and evaluation harnesses, analyze feedback, and mentor teammates to translate models into measurable product impact.

As a Generative AI Consultant, design and implement GenAI solutions, assist in architecture, develop applications, and ensure responsible AI practices while collaborating across teams.

Lead design and delivery of AI agent solutions that convert documents into structured data for Veeva Quality Cloud. Define data flows, orchestration, model integration, and prompts; collaborate with engineering and UX to automate processes, evaluate AI agents, and present features to stakeholders.

Lead design and delivery of AI Agent solutions for Veeva QualityDocs and Training. Define data flows, orchestration, prompts, and model integration; collaborate with engineering and UX; evaluate AI agents and present capabilities to stakeholders.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.