Top AI Jobs in Toronto

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

Design and build predictive models and machine learning solutions using big-data technologies. Mine structured and unstructured data, develop end-to-end scoring pipelines, collaborate with product teams and stakeholders, and provide data-driven recommendations to senior leaders to improve customer experience and business outcomes.

Lead technical direction and architecture for DAP Repository Flows, design and ship autonomous and scheduled agents that operate on customer repositories, improve DAP onboarding, collaborate across teams on platform interfaces, mentor engineers, and solve high-scale technical problems to raise engineering quality.

Lead and own the end-to-end Registry design experience, set vision and KPIs, hire and develop a small design team, use AI-native workflows to prototype and ship cross-platform consumer products, partner with PMs/engineers/analytics, and scale design systems and processes to drive measurable business impact.

Lead and mentor a team of applied ML scientists to design, build, and deliver predictive AI solutions (regression, classification, anomaly detection) across personal banking. Collaborate with engineers, research scientists (Layer 6), and stakeholders to translate business objectives into scalable models, ensure governance and compliance, monitor systems, and drive continuous improvement and innovation.

Supports Operational Risk AI initiatives by identifying and documenting AI use cases, gathering requirements, and partnering with AI and technology teams throughout the development lifecycle. The intern will experiment with large language models, apply prompt engineering, create Python scripts, support AI adoption as a change champion, and communicate with risk subject matter experts. This is a four-month Winter 2027 student placement based in Ontario, with flexible virtual, hybrid, or in-office arrangements.

Build and deploy secure AI agents, copilots, and RAG workflows across Azure and AWS. Partner with business teams to identify high-value use cases, prototype solutions using Python and LLM APIs, operationalize cloud deployments, and create reusable self-service patterns. Collaborate with Security, Privacy, and Governance to implement responsible AI, access controls, monitoring, evaluation, and human-in-the-loop practices. Translate technical concepts for non-technical users and help business teams safely adopt AI capabilities.

Lead AI strategy, roadmap, and product management across Business Technology. Build and scale AI capabilities, mature product management practices, mentor teams, ensure ethical AI, measure ROI, and manage vendors and budgets to deliver business impact.

Design, develop, deploy, and optimize machine learning and deep learning models for customer-facing applications. Build data collection, preprocessing, training, and evaluation pipelines; conduct experiments; analyze data; and improve model performance and scalability through techniques such as quantization, distillation, and edge deployment. Collaborate with product, design, and engineering teams, contribute to system architecture, participate in code reviews, and apply relevant AI research to production solutions.

Identify and prioritize AI and automation opportunities, analyze workflows, build and pilot practical AI solutions, and maintain an AI roadmap. Track adoption, productivity gains, ROI, and business impact while advising teams on AI use. Develop training materials and executive presentations, gather feedback, and support responsible AI governance, privacy, and risk management. The role requires hands-on software engineering and agentic AI experience using Python or TypeScript and relevant orchestration frameworks.

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.

Develop and integrate generative AI applications using hosted foundation models, RAG, knowledge graphs, prompt and context engineering, and agentic workflows. Build tool-using agents with frameworks and protocols such as LangGraph, Google ADK, MCP, and A2A. Support API development, document ingestion, deployment, monitoring, testing, documentation, and production maintenance while collaborating with data scientists, engineers, architects, and business stakeholders.

Leads and mentors data engineering teams while designing, deploying, and operating scalable data pipelines and platforms supporting analytics, AI/ML, and commercial use cases. Provides architectural leadership across cloud-native platforms, distributed processing, orchestration, data integration, and streaming architectures. Manages delivery priorities, stakeholder requirements, operational excellence, incident response, standards, and continuous improvement across enterprise data engineering initiatives.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Conduct research and design AI data-security and privacy architectures for endpoint devices. Prototype and build privacy-preserving defenses, analyze emerging attack vectors and regulations, and collaborate with global labs and product teams to transition research into production-grade endpoint security solutions.

Intern will help design and implement the openJiuwen AI Agent Platform and enterprise data foundations: knowledge graphs, vector DBs, workflows, agent frameworks, tool-calling, and multi-agent systems. Contribute to open-source projects (JiuwenSwarm), build real-world AI agents, explore LLM, RAG, memory, planning, and evaluation technologies, and support documentation and community collaboration.

Research and analyze state-of-the-art AI model architectures and training/inference workloads; map workloads to hardware requirements; develop performance models and deployment strategies; profile hardware/software, evaluate algorithm-hardware tradeoffs, and provide quantitative recommendations for next-generation AI accelerators.

The engineer will deploy Okta's agent security solutions at customer locations, collaborate with teams, and ensure agent identity and security protocols are met.

As a Principal Forward Deployed Engineer at Okta, you lead strategic deployments, shape reference architecture, coach teams, and represent technical authority on agent identity to senior executives.

As a Senior Software Engineer, you will design, implement, and maintain backend systems that integrate identity and authorization capabilities into AI workflows, ensuring security and reliability at scale.

Lead design, configuration, and operation of AI-powered sales tools and workflows. Translate field needs into technical requirements, own the AI sales roadmap, deploy and iterate integrations and automations, measure adoption and revenue impact, and coordinate cross-functional stakeholders to scale AI use cases across the sales lifecycle.

Senior pre-sales AI subject-matter expert for legal customers who designs and delivers demos, proofs-of-concept, and workshops; partners with account teams to shape deal strategy; educates clients on features/releases; builds success plans to drive retention and expansion; represents the company at industry events; and translates emerging generative AI capabilities into practical legal use cases.

Build backend infrastructure and APIs to integrate Lyft with emerging AI ecosystems and devices. Design scalable distributed systems, mentor engineers, drive high-impact projects, write and review maintainable code, participate in on-call rotation, and collaborate cross-functionally to deliver production-grade AI-enabled features.

Lead a team of up to 10 engineers to deliver AI workflow solutions for R&D. Drive technical vision, ensure quality, establish standards, recruit and develop staff, manage vendors, and collaborate with product and scientific stakeholders to execute the team's roadmap.

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.