Top AI Jobs in Toronto

Join a talent network connecting senior/staff applied AI scientists to VC-backed startups. Responsibilities include researching and developing ML methods, fine-tuning foundation models, designing experiments and evaluation frameworks, building prototypes, collaborating with engineering/product to productionize models, improving model robustness and efficiency, curating datasets, investigating failures, mentoring peers, and communicating results to stakeholders.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Join a talent network connecting founding AI engineers with VC-backed startups. Responsibilities include designing, building, and deploying ML/DL models, creating scalable data pipelines, optimizing real-time and batch model deployments, developing RAG and LLM systems, and aligning AI strategy with founders and product goals.

Build, deploy, and scale AI-powered automations and agentic workflows across multiple retail brands. Connect APIs and platforms, write Python/JavaScript scripts, integrate LLMs and automation tools (n8n/Zapier/Make), train non-technical teams, and expand proven solutions cross-brand to reduce manual work and improve ROI.

Architect and lead end-to-end production AI solutions across cloud, edge, robotics, and IIoT. Drive AI strategy, product lifecycle, MLOps, model development, deployment, and optimization. Integrate foundation models, multimodal and agentic AI with robotics, sensors, and digital twins. Ensure security, governance, observability, and operational excellence. Mentor teams, coordinate cross-functional delivery, produce technical/service documentation, and translate innovation into commercial value.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Design, build, deploy, and support scalable backend APIs and agentic AI solutions using Go. Architect microservices and cloud infrastructure (Azure/GCP), integrate data platforms, create API docs and partner enablement assets, and ensure operational readiness with CI/CD, SLOs, and security-by-design.

Design, develop, fine-tune, and optimize generative deep learning models (GANs, VAEs, transformers). Collaborate with cross-functional teams to integrate and deploy AI solutions, troubleshoot model issues, document models, and communicate technical concepts to non-technical stakeholders.

Lead product marketing for Procore AI & Data products: own positioning, messaging, launches, competitive positioning, sales enablement, and cross-functional GTM programs. Collaborate with Product, Demand Gen, Sales, and global Marketing to create customer-facing content and drive adoption.

Design, develop, and deploy generative AI solutions (LLMs, RAG, vector DBs, agentic AI) for financial applications. Build AI agents, implement RAG pipelines, fine-tune and evaluate models, collaborate with engineering to productionize models (CI/CD, containers, cloud), and communicate results to stakeholders.

Assist in deploying and monitoring Cerebras AI infrastructure, perform troubleshooting, collect telemetry, and learn through shadowing senior engineers.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

Analyze and document underwriting workflows, collaborate with Product Owners and SMEs to design optimized AI-driven underwriting solutions, translate business needs into actionable requirements, manage change and UAT, evaluate product outcomes, and support solutions from inception through deployment to ensure business value.

Lead integration and product quality assurance for AI-driven P&C underwriting solutions. Translate underwriting business needs into clear requirements, coordinate cross-functional teams, drive issue resolution, support lifecycle from inception to deployment, and recommend workflow optimizations using underwriting expertise.

Validate and research Generative AI and agentic systems, design and build enterprise AI applications (LLMs, RAG, agents), develop scalable software and automation, execute quantitative testing and validation, and maintain documentation to support governance and audit readiness.

Senior commercial leader accountable for P&L, revenue growth and market share across Large Law, Mid Law and Quebec. Define and execute commercial and go-to-market strategy, build and scale sales teams, drive AI adoption of LexisNexis solutions, manage pipeline/forecasting, inform product strategy from customer insights, and align cross-functional stakeholders to accelerate adoption and profitability.

Design, build, and productionize agentic systems: agent loops, orchestration, memory, tool integrations, and multi-agent workflows. Improve reliability by diagnosing emergent failure modes, instituting guardrails, and continuously evaluating agent behavior. Optimize tradeoffs among latency, quality, and token cost, and integrate agents with systems of record for business-grade production deployment.

Lead design and delivery of cloud-native AI applications (agentic AI, RAG, LLM integration). Build and optimize RAG pipelines, vector DBs, and multi-agent orchestration. Implement MLOps/LLMOps, CI/CD, security and governance, model evaluation and optimization. Provide technical leadership, mentor teams, translate business needs into AI solutions, and ensure alignment with risk and regulatory frameworks.

As an AI Engineer, you'll design and deliver agentic systems that integrate Large Language Models to enhance business workflows and operational efficiency, providing hands-on technical leadership and creating enterprise-grade AI solutions.

Lead enterprise Responsible AI and AI governance: develop standards, policies, evaluation frameworks and KPIs; review AI use cases, manage risks, advise stakeholders and regulators, drive continuous improvement, and lead a team to implement governance across the bank.

The Enterprise Architect leads the development of AI and Data architecture, aligning technology with business needs while ensuring adherence to governance standards. They provide strategic guidance, collaborate with stakeholders, and utilize advanced technologies to enhance solutions and processes.

Design and enforce AI security across the model lifecycle (training, deployment, inference). Develop AI threat models, integrate AI into Zero Trust and cloud/data protection frameworks, perform architecture reviews, support vendor assessments, ensure regulatory and audit readiness, and enable secure AI adoption through guidance and cross-functional collaboration.

Develop, deploy, and own RTL-to-GDSII physical design methodology and CAD flows for IP delivery. Automate PPA exploration, document flows, partner with design teams and EDA vendors, qualify new nodes/PDKs and EDA releases, and apply ML/LLMs where they improve flow optimization and scalability.

Design and validate high-bandwidth interconnects for AI clusters across copper and optical media. Define architectures, perform link and channel budget analysis, lead qualification and reliability testing, develop validation plans for transceivers and cables up to 1.6T, and coordinate with suppliers and manufacturing.

Build and ship LLM-powered features and agentic workflows for a financial platform. Own data pipelines, retrieval architectures, context design, and MLOps to move models from prototype to production. Collaborate with domain experts, evaluate experiments, and contribute to AI Lab tooling and practices.