Top AI Jobs in Toronto

The AI Architect will design enterprise-grade AI solutions using Generative AI, LLMs, and orchestration frameworks, focusing on building scalable systems and developing production-ready applications.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and implement advanced generative AI models, optimize deployment in production, collaborate with teams, and stay updated on AI advancements.

Lead large-scale AI infrastructure deployment programs, manage project execution for GPU clusters and data center infrastructure, and mentor junior project managers.

The engineer supports simple data hall designs, drafts AutoCAD drawings, assists with capacity planning, and learns data center principles under supervision.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Design and build AI-driven interactive prototypes and production-ready flows using AI coding tools. Own prompt-to-pipeline solutions, validate via experiments and user research, produce high-fidelity UX in Figma, and collaborate with engineering to move prototypes to production while aligning business, design, and tech.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Lead live sessions, office hours, and project reviews for senior engineers transitioning into system architecture. Provide detailed architecture critiques, mentor students one-on-one, run architecture review boards, record instructional videos, conduct mock interviews, and coordinate supporting instructors to ensure high technical standards and student readiness for architecture roles.

Author and refine a systems-engineering curriculum for experienced engineers: shape syllabus, define learning objectives, produce and fact-check AI-assisted lessons, design hands-on projects, write rubrics and reviewer guides, and iterate with an LX designer. Part-time (15-20 hrs/week) for ~4-6 months.

Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

Lead and mentor an AI engineering team to design, build, and deploy production-grade LLM, NLP, and generative AI solutions on Azure. Architect RAG frameworks, conversational agents, and scalable AI workflows; collaborate with stakeholders to translate business needs into reliable, monitored AI services.

Lead and mentor an AI engineering team to design, build, and deploy production-grade LLM, NLP, and generative AI solutions on Azure. Architect RAG frameworks, conversational agents, and scalable AI workflows; engage stakeholders, validate model performance, and support AI governance and continuous improvement.

Lead and mentor an AI engineering team to design, build, and deploy production-grade LLM, NLP, and generative AI solutions. Architect Azure-based AI systems (Azure OpenAI, Azure AI Services), implement RAG and vector DB workflows, develop conversational agents, collaborate with stakeholders, and establish testing, monitoring, and governance for continuous improvement and reliability.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Lead design and implementation of Northleaf's AI-ready knowledge architecture: taxonomy, metadata, content governance, onboarding, and quality testing to enable secure, repeatable AI/search workflows across SharePoint and Azure-based platforms.

Design, prototype and deliver agentic AI solutions and automations using Microsoft Copilot, Copilot Studio and Power Platform. Partner with business teams to map processes, build production-ready agents, enable integrations, and create reusable templates and orchestration patterns to drive adoption and enterprise-scale automation.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Design and implement advanced Generative AI models, optimize performance for LLMs, and collaborate with cross-functional teams to innovate AI solutions within the automotive sector.

As a Backend/AI Systems Engineer, you will develop backend systems for AI workflows and enhance core functionalities for Spellbook's legal software. You'll tackle complex problems, ensure system reliability, and collaborate with product teams, while maintaining a hands-on ownership approach.