Top AI Jobs in Toronto

The role involves developing AI technology for contract management, analyzing contracts, collaborating with teams, and automating processes.

Lead the design and delivery of cloud-native AI applications, fine-tuning large language models, establishing best practices, and partnering with cross-functional teams.

The Product Manager will lead AI platform initiatives, define integration strategies for AI agents, and engage with customers to enhance user experiences.

The Director of AI will define the AI strategy, manage R&D efforts, and drive organizational adoption of AI in game development and operations. Responsibilities include setting priorities, leading cross-functional initiatives, and establishing metrics for success.

The Sr. Manager, CRM AI Transformation will drive the vision for eBay's CRM transformation, focusing on enhancing customer experiences through strategic, technical, and operational initiatives across multiple teams.

Lead data and AI transformation programs, partnering with executives to define strategies and deliver modern data platforms and solutions, while driving business growth and ensuring excellence.

Serve as the finance partner to the SVP of Autodesk Research, managing investments in Generative AI while building financial models and coordinating across finance teams.

The Director, AI Enablement will define and execute AI product strategy, enhance AI applications, collaborate with various teams, and ensure alignment with CIBC Mellon's objectives.

As a Principal Software Engineer, lead initiatives for the AI platform, manage Kubernetes environments, and develop infrastructure automation using Terraform. Collaborate with teams to design scalable solutions and ensure deployment compliance.

Architect and build scalable, secure cloud-native services and AI workflows. Define model strategies, establish standards, and partner with cross-functional teams.

The role involves building scalable cloud-native systems for AI-driven workflows, integrating AI features, and ensuring high code quality and system reliability.

Manage AI compute clusters, monitor systems health, optimize resources, and troubleshoot technical issues, ensuring high performance for ML applications.

Design and develop frontend and backend applications, ensuring seamless integration, implementing security measures, and collaborating with clients and AI Engineers.

As a Staff Platform Engineer, you will design and build cloud-native platforms, develop scalable infrastructure, and collaborate with various teams in the company to innovate AI-driven solutions.

Develop AI-driven software systems by architecting and implementing advanced AI technologies, integrating models, mentoring engineers, and ensuring quality and performance.

As a Senior MLOps Engineer, you'll optimize AI/ML platform operations, automate model deployments, and ensure model performance and security while collaborating across teams.

The Associate AI Engineer will oversee the data lifecycle, develop data asset processes, and manage integration while enhancing data quality and analytics tools. They will also support cloud data management implementations and work with stakeholders to resolve data issues.

The Associate AI DevOps Engineer designs data architectures, manages data securely, implements cloud platforms, and builds data pipelines. Responsible for optimizations and analytics for operational improvements.

The role involves managing HPC infrastructure, deploying automation tools, troubleshooting issues, and supporting ML teams in optimizing cluster operations.

Design and optimize high-performance networking infrastructure for AI/ML with tasks including troubleshooting, performance monitoring, and network upgrades.

The Product Marketing Manager will own go-to-market strategies and lead product launch planning while collaborating with teams to enhance marketing assets and support customer education.

The Director of AI will lead the integration of AI solutions, guide clients through implementations, design scalable AI applications, and define best practices within a cross-functional team.

The AI Architect will design, code, and deploy hands-on AI solutions, leveraging AWS and Agentic AI patterns while leading technical team growth.

The Deployment Engineer will manage AI inference clusters, optimizing deployment, capacity allocation, and ensuring reliability of pipeline operations across datacenters.

Lead product management for AI and automation, developing strategy and integrating with operations. Focus on customer value, team performance, and market impact.