Top AI Jobs in Toronto

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.

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 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.

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.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Lead operational rhythms and client delivery, focusing on AI solutions. Collaborate with executives and manage multi-stakeholder projects for positive outcomes.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead the design and delivery of an end-to-end AI system layer across the product: agentic workflows, orchestration, evaluation, guardrails, observability, and developer platform. Build and grow a multi-team org, partner with Product and Design, define how AI decisions flow across cross-product context, and ensure reliable, measurable, and safe autonomous behaviors that proactively help small-business users.

Perform hands-on product and player analytics across onboarding, retention, monetization, progression and LiveOps. Build and QA ETL pipelines in an AWS analytics stack, write cost- and performance-aware SQL, support A/B testing, and create reusable AI-enabled analytical workflows and notebooks to inform product, marketing, and LiveOps decisions.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The role involves developing and productionizing security detection models, collaborating with security research, and shaping engineering culture as a founding team member.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Lead engineering to shift GTM from no-code automation to code-first, AI-agent-driven systems. Design scalable backend pipelines, integrate CRMs/APIs, build reusable agent frameworks, ensure reliability and observability, and hire/mentor a distributed engineering team to deliver production GTM infrastructure.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Lead high-volume outbound prospecting, run discovery calls, qualify and manage high-ticket opportunities through the full sales cycle, book and conduct meetings, handle objections, collaborate with founders/sales leadership, and consistently meet outreach, meeting, and revenue KPIs.

The Senior AI Solutions Engineer will architect and deploy AI systems on Google Cloud, collaborating with clients and delivery teams to create enterprise-ready solutions.