Top AI Jobs in Toronto

The Lead AI Forward Engineer designs AI solutions, guiding implementations for scalability and compliance, while mentoring teams and shaping technology strategy.

Build and own end-to-end AI-enabled internal tools and workflows for game content production. Design data models, backend services, APIs, orchestration, human-in-the-loop review, and UIs. Implement reliable, observable systems with approval gates, retries, audit trails, and staged processing. Partner with cross-functional teams to turn ambiguous goals into production-ready solutions and maintain high quality, maintainability, and engineering standards.

Design and deliver data analytics and AI solutions for Capital Markets. Apply statistical and machine learning models, build AI agents and dashboards, produce insights for senior management, and collaborate with data engineers and business teams to ensure governance and integration.

The role involves leading projects in AEC data modeling and machine learning, mentoring junior engineers, and collaborating to enhance design workflows.

As a Senior Software Engineer, you will develop AI-powered systems to enhance accounting workflows, build backend services, and ensure optimal performance and reliability of AI solutions.

As an AI Specialist, you will optimize AI initiatives, enhance system performance, develop ML algorithms, and collaborate with researchers on quantum hardware projects.

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.

The AI Engineering Lead will spearhead AI-powered solutions for fraud detection in insurance, oversee system architecture, mentor engineers, and ensure fair AI practices in fraud mitigation.

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.

Lead design, development, and production deployment of AI/ML capabilities for a commercial real estate SaaS platform. Build LLM-powered features and custom models, establish AI standards and infrastructure, mentor engineers, drive applied research, and partner with product, data, and engineering teams to deliver scalable, responsible AI solutions.

Embed within the M&A team to identify, prioritize, prototype, and deploy AI and data-science solutions that speed deal sourcing, diligence, pricing, and integration. Architect the data foundation, translate between business and technical teams, build ROI-backed use cases, ship prototypes into production, drive adoption, and measure model performance, time saved, decision quality, and dollar impact across the transaction lifecycle.

Executive leader responsible for enterprise technology, data and AI strategy, operating model and ERP transformation. Build scalable data platforms, advance analytics, automation and LLM capabilities, ensure governance and security, drive business partnering and measurable value, and lead a global technology and data organization.

The Senior Software Engineer will develop LLM-powered agents for search and retrieval systems, optimize backend architectures and workflows, and evaluate agent performance while collaborating with teams to enhance customer experience.

As a Software Engineer, AI at Klue, you will design and implement LLM-powered retrieval systems, optimize workflows, and collaborate closely with product teams.

Design, develop, and deploy specialized AI agents and tool integrations to automate design and development workflows. Build interfaces (MCP/Function Calling), perform prompt engineering, enforce accessibility and quality standards, collaborate with product/design/engineering, and extract metrics from agentic workflows to improve UX and compliance.

The Staff Software Engineer will lead the design and delivery of AI-driven backend systems, influencing technical strategy, driving cross-team initiatives, and mentoring engineers.

Design and develop scalable backend services and APIs for an AI-powered platform, focusing on system architecture and mentoring junior engineers.

The Senior Applied Scientist will lead research projects in AI/ML for various legal and taxation solutions, focusing on developing scalable AI capabilities while mentoring junior team members.

Lead design and delivery of scalable data platforms and ETL/ELT pipelines on Azure to support analytics and AI workflows. Build data models, ensure data governance and quality, integrate LLMs/AI tools, enable real-time ingestion and observability, and translate business requirements into analytics solutions for capital markets stakeholders.

The Principal Solutions Architect will lead technical architectures, drive AI-focused solutions, collaborate across teams, and mentor others to guide customers in maximizing GitLab’s DevSecOps platform's value.

Design, develop, test and maintain GenAI/ML components and applications. Implement new features, perform unit tests, fix defects, participate in code reviews, incident response, and collaborate with stakeholders to ensure scalable, secure engineering solutions and continuous improvement.

Develop and deploy deep learning models across vision, language, audio, and multimodal domains; build end-to-end workflows from data curation to on-device or cloud deployment; prototype and integrate generative and foundation-model research into product features; collaborate cross-functionally and publish or patent innovations.

Lead end-to-end product experience design for AI-powered, multimodal interactions. Translate research into product scenarios, create wireframes and high-fidelity prototypes, specify interactions, and collaborate with researchers and engineers to validate and move concepts toward product implementation.

Lead delivery and lifecycle management of the enterprise CAI platform and sub-products. Manage multiple development and QA squads, vendor relationships, release and quality governance, and stakeholder collaboration to translate business needs into reliable, scalable AI/ML solutions while ensuring compliance and operational excellence.

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.