Top AI Jobs in Toronto

The GenAI Platform Engineer will design, develop, and maintain Generative AI platforms using Python, Node.js, and TypeScript, ensuring scalability and integration across Citi's business units while mentoring junior members and collaborating on innovative projects.

Build and optimize conversion-focused web experiences, landing pages, demos, and growth systems. Instrument analytics, attribution, and experimentation; integrate APIs and automate workflows; apply AI to content, personalization, and experiment generation; and partner with Growth, Marketing, and Sales to drive qualified pipeline and measurable revenue outcomes.

Own model selection, evaluation, and deployment for voice and multimodal restaurant AI agents. Build datasets, experiments, and monitoring to improve accuracy, safety, latency, and cost. Partner with product and engineering to ship model changes with guardrails, rollouts, and clear metrics tied to business outcomes.

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.

Build and operate scalable, secure cloud infrastructure for real-time AI services. Improve reliability with SLOs, observability, capacity planning, and failure testing. Implement IaC, CI/CD, deployment safety, secrets and vulnerability management. Partner with product and AI teams, diagnose distributed failures, reduce cost, and automate platform patterns to enable faster, safer releases.

Build end-to-end AI-driven restaurant products from discovery through production. Own full-stack interfaces, backend services, agent workflows, integrations (POS, reservations, telephony), evaluation systems, and instrumentation. Design agent workflows combining models, rules, memory, and human escalation. Work directly with product, design, and restaurant users to iterate, improve reliability, latency, accuracy, and cost, and ship production-quality solutions.

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.

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 Developer will build and maintain data pipelines, design RAG systems, develop AI workflows, and ensure production reliability in healthcare settings.

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.

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.

The engineer will develop features for an AI workspace platform, deploy AI solutions in public sector environments, and interact with clients.

Lead the design and deployment of AI/ML solutions for grid automation, manage teams, drive innovation, and ensure adherence to architectural standards.

Develop, deploy, and maintain AI-powered applications and intelligent workflows using Python in cloud-native environments. Build APIs, microservices, and data pipelines; integrate LLMs and ML models; implement retrieval/prompt-driven experiences and CI/CD; optimize performance, security, and scalability; collaborate cross-functionally and troubleshoot model and system issues.

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

The AI Automation Engineer will automate internal processes, mentor non-technical staff on building tools, and ensure solutions deliver measurable business impact using AI technologies.

The Senior Frontend Engineer will lead development of UI features using React and TypeScript, collaborate on API designs, and optimize performance in a data-intensive financial platform environment.

Design, build, and operate scalable Generative AI systems and RAG pipelines; own end-to-end AI application lifecycle on Databricks and AKS; integrate LLMs with data sources and APIs; implement LLMOps, evaluation, observability, and production monitoring; mentor engineers and influence product roadmaps.

The role involves building scalable AI solutions, enhancing productivity through agentic automations, and integrating LLMs and other advanced AI frameworks.

Lead and manage teams to design, build, and deploy AI/ML systems with emphasis on LLMs, RAG, NLP, and MLOps. Drive technical strategy, resource and budget planning, standards, risk assessment, and cross-functional influence while ensuring compliance and supervisory accountability.

Lead enterprise Generative AI strategy and engineering: select and deploy LLMs, design RAG and agentic systems, build production GenAI services, establish governance, observability, cost controls, and reusable accelerators while managing senior engineering teams and cross‑functional stakeholders.

The AI Quality Assurance Intern will evaluate AI systems' behavior and accuracy, ensuring they perform as intended and meet business logic requirements.

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.

As a Machine Learning Engineer, you will develop user experiences using LLMs and predictive analysis, deliver ML solutions, and ensure continuous improvement in AI products at scale.

Lead enterprise product vision and multi-year strategy for integrated product strategy and AI across ABC's portfolio. Build and manage a high-performing product and UX team, define AI-enabled platform and API-first capabilities, partner cross-functionally on go-to-market and technical decisions, and drive platform convergence, monetization, and measurable business outcomes.