Top AI Jobs in Toronto

Build and maintain high-throughput, low-latency backend services for Life360’s AdServer and Exchange platform. Own projects from architecture and implementation through deployment, improve real-time bidding and auction logic, integrate advertising partners, and support observability, incident response, and reliability. Collaborate with architects and product teams, use AI coding tools daily, mentor junior engineers, and contribute to engineering standards.

Design, prototype, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly iterate with cross-functional teams to integrate AI into incident detection, triage, and automated analysis. Build scalable, production-ready solutions in cloud-native environments and use observability tooling to troubleshoot and improve system behavior. Own end-to-end delivery and collaborate on product, data, and infra integration.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

Build and maintain internal AI enablement tools, workflows, and guardrails to help engineers use coding agents and LLMs safely. Evaluate models, partner with teams to apply AI, track adoption and impact, and provide documentation, coaching, and advocacy to increase engineering velocity.

Lead global R&D to define technical strategy and roadmaps, prioritize AI-driven modernization, manage engineering and test teams, oversee AI/ML model validation in regulated clinical environments, own R&D budgeting and vendor partnerships, ensure regulatory compliance, and drive operational excellence and ethical AI adoption to improve clinical trial outcomes.

Lead strategy and delivery of AI-powered capabilities across Employ's recruiting platforms. Define product vision, prioritize roadmap, partner with engineering, design, and data science, ensure responsible AI practices, measure product success, and drive adoption to improve recruiter productivity and customer outcomes.

Lead discovery, prototype, and productize ML/AI and edge computing innovations. Build proofs of concept, collaborate with cross-functional teams, engage customers/partners for co-innovation, influence product roadmaps, and evangelize solutions to drive commercialization and market adoption.

Lead end-to-end development and production ownership of AI-driven backend capabilities and LLM/voice agent workflows. Improve reliability, observability, security, and operational maturity while iterating quickly from customer feedback and scaling team delivery.

Lead design, build, and operate scalable Generative AI systems (LLM apps, RAG pipelines, multi-agent systems) in production using Databricks and cloud infrastructure. Own CI/CD, monitoring, evaluation frameworks, optimize retrieval/generation, mentor engineers, and influence product roadmap.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Lead architecture and performance exploration for next-generation AI inference accelerators. Build analytical models and simulation frameworks, define subsystem interfaces and roadmaps, and drive hardware/software co-design with compiler, runtime, ML, and silicon teams to optimize latency, throughput, memory, interconnects, power, and cost for evolving LLMs and transformer-based workloads.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Embed with enterprise customers to design, build, and deploy production agentic AI workflows. Integrate platform with customer systems, handle security and compliance reviews, drive adoption and expansion, and feed product improvements based on field signal.

Embed with enterprise customers to deploy production agentic AI workflows: integrate data sources, identity, CRMs and APIs; handle security, SSO/SCIM, isolated VMs and compliance; drive adoption, instrument usage, and feed product improvements to scale deployments.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

As a Staff Software Engineer, you'll design and deliver a core data platform, focusing on architecture, pipeline components, and AI integration. This hands-on role emphasizes creating production code and establishing technical standards within a small team.

Sell AI-powered solutions to automotive dealerships in the Maritimes. Own the full sales cycle—prospecting, discovery, demo, negotiation, and close—build relationships with dealer leadership, present ROI-focused demos and proposals, maintain pipeline, forecast accurately, and consistently meet revenue targets.

Own and automate the end-to-end ML lifecycle: build, deploy, monitor, test, and retire models at scale. Design production-grade, highly available ML infrastructure and CI/CD pipelines, implement governance, and collaborate with engineering and security teams.

Design and run automation-driven, AI-agent-assisted attack campaigns and red-team simulations against Wealthsimple products and infrastructure; build and improve automated testing pipelines, AI agents, and tooling; triage AI-generated findings into high-signal vulnerabilities; collaborate with platform engineers and AppSec to integrate adversarial testing into the SDLC and support remediation.

Build, operate, and scale cloud-native backend services and APIs for a high-volume vehicle marketplace. Embed AI-driven capabilities into features, use AI tools to accelerate development, participate in on-call rotations, improve reliability, and collaborate with Product, Data, Infrastructure, and SRE teams to deliver production-ready systems.

Build and maintain FacilityOS's AI agent architecture: RAG pipelines, agent orchestration, integrations with Microsoft 365/Outlook/Teams, secure auth, evaluation harnesses, compliance-ready deployment, and cross-functional enablement for department-owned agents.

Design, build, and deploy agentic AI workflows, LLM-enabled services, and cloud-native platform components. Integrate AI capabilities with enterprise systems, ensure production readiness, implement guardrails, testing, and monitoring, and collaborate with architects, delivery managers, and stakeholders to deliver secure, scalable AI-enabled automation and workflow solutions.

Lead the development and architecture of voice biometric systems and conversational AI agents, ensuring secure and sophisticated interactions.