Top AI Jobs in Toronto

Lead Mastercard's AI thought leadership: monitor AI/data trends, produce research and executive content, manage end-to-end content lifecycle, measure program impact, and represent the company at events while partnering with product, engineering, marketing, and executives.

Build and enhance AI and data solutions for insurance by collaborating with data architects, scientists, and engineers. Use AI-first tools and agents, design prompts, prototype generative AI, review AI-generated code for security and quality, track production trends, document patterns, participate in code reviews, and assist senior staff to scale AI capabilities across the platform.

The Engineering Manager leads Data Engineering teams, developing scalable data solutions and collaborating with stakeholders to drive McCain's digital transformation.

Design and implement ML/AI models and big-data solutions, mine structured and unstructured data, and build Power BI dashboards and visualizations. Provide analytical insights and recommendations to business leaders, collaborate with analytics teams to scale solutions, drive process improvement, and apply risk management in decision-making. Typically focused on a business group and works mostly independently.

Lead strategy and roadmap for CrowdStrike's Charlotte GenAI platform, deliver developer-first AI capabilities, partner with engineering and data science, drive cross-functional delivery and adoption, evangelize GenAI across the Falcon ecosystem, and ensure platform scalability, performance, and security.

Build and own core components of an LLM-powered agent platform: orchestration, tool integrations, APIs, knowledge/memory infrastructure, trust & safety, and evaluation tooling. Partner with Operations to translate workflows into agent capabilities, mentor teammates, and ship production-ready, scalable AI-native systems.

Lead the evolution of the recommendations engine system into a scalable distributed architecture, ensuring production stability and performance while mentoring engineers and collaborating with ML teams.

Own, build, and optimize Spark-based production data pipelines and ML data lake on GCP Dataproc to support recommender systems. Collaborate with ML engineers, resolve performance bottlenecks, and deliver scalable, reliable data products for AI-driven personalization.

Support Market Risk Solutions by analyzing market risk processes, documenting current/future states, proposing solutions, managing projects, executing testing, and engaging stakeholders to deliver regulatory and internal market risk measurement, reporting, and capital calculation capabilities.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

As an AI Transformation Owner at GitLab, you will shape AI strategies, improve workflows, and drive adoption of AI solutions in your organization, working closely with an AI Engineer.

Lead technical vision and architecture for AI-powered GTM systems, defining reference architectures, patterns, and governance for low-code and custom agentic solutions. Architect and prototype LLM orchestration, multi-system integrations, RAG pipelines, and event-driven data flows across Salesforce, NetSuite, Marketo, and cloud infrastructure while partnering with GTM leaders to deliver production-grade, auditable, and scalable automation.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Work as an AI-focused intern to develop, train, validate, and deploy machine learning and computer-vision models (YOLO, Python, OpenCV, TensorFlow/PyTorch) for production-floor systems. Collaborate with cross-functional teams to gather requirements, monitor and improve model performance, document methods, report progress, and provide training and support for AI initiatives.

Test and validate AI models and software for automotive use cases: generate ground truth, build annotated datasets and testing frameworks, evaluate technologies, support R&D engineers, coordinate projects and outsourced work, and collaborate with internal and external partners to drive innovation and product readiness.

Build and maintain backend features for Agent Observability across the GitLab monolith and AI gateway. Design and implement GraphQL/REST APIs, improve automated tests (RSpec), collaborate cross-functionally, troubleshoot production issues on Tier 2 on-call, and help shape responsible AI integration and platform architecture.

Provide commercial legal support for global SaaS deals: draft and negotiate MSAs, SLAs, NDAs, SOWs and AI/data privacy terms; advise Sales, Dealdesk, Partnerships and Finance; create templates, run contract reviews, support RFPs and reseller agreements, and improve contract management processes.

Manage a cross-functional engineering team building GitLab Duo Chat's AI-powered features. Define requirements with product and UX, contribute to architecture for scalable agentic AI, drive delivery and roadmap execution, improve testing, observability, performance, and coach engineers to foster an inclusive, high-performing team.

Lead AI-native engineering teams to design, build, test, and deploy large-scale, data-oriented, customer-facing applications using Generative AI, LLMs, and agentic patterns. Define engineering vision and AI-native SDLC, partner with product, leverage data technologies (e.g., Databricks), mentor talent, and drive 0→1 prototypes to production with strong backend (Java/Python) and frontend (React/Next.js) engineering.

Lead architecture and productionalization of AI-native, large-scale, data-oriented product concepts. Design scalable solutions, implement AI systems with observability and cost optimization, establish responsible AI practices, and coach engineering teams across the product development lifecycle.

Design, build, and scale production GenAI services (MCP Server/Gateway, AI Agent). Own end-to-end features, ensure reliability, performance, and security, use AI-assisted development tools, collaborate with product/design, and mentor teammates while producing documentation and monitoring systems.

Design and develop Large Language Model-based applications within Pharma R&D, focusing on client collaboration and technical excellence while ensuring production readiness and performance optimization.

Lead Fraud and Client Account Management analytics to deliver insights, roadmap and automation. Develop collections models and performance monitoring, ensure data quality, apply advanced statistics and AI/ML, build reporting platforms, and drive cross-functional partnerships while growing team capabilities to optimize collections risk and returns.

As an Applied Scientist, you will push the limits of AI/ML in home valuation, tackling diverse ML challenges and optimizing decision-making with data.