Top AI Jobs in Toronto

The Instructional Designer is responsible for creating engaging learning experiences, managing the instructional design lifecycle, and collaborating with SMEs and developers to deliver effective training programs.

Part-time SEO & AI Search Specialist responsible for technical SEO audits, keyword and content strategy, schema implementation, AI search optimization, high-quality backlink acquisition, and monthly performance reporting to increase qualified organic traffic.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Design and implement robust bimanual manipulation algorithms and train learning-based robot policies (diffusion, imitation, RL). Build end-to-end autonomy connecting perception, semantic task understanding, and low-level control. Ship production-quality Python and C++ code, profile and scale the autonomy stack, and work with high-fidelity simulators for sim-to-real transfer.

Architect and implement robust bimanual manipulation algorithms and learning-based robot policies (diffusion, imitation, RL). Build end-to-end autonomy connecting perception to low-level control, write and maintain production Python/C++ code, and scale performance across a fleet in industrial environments.

Design and implement robust bimanual manipulation algorithms and learning-based robot policies. Build end-to-end autonomy connecting perception, task understanding, and low-level control. Ship production-grade Python and C++ code, optimize system performance, and scale deployments across an industrial robot fleet.

Build end-to-end AI product features across frontend and backend: design LLM agent systems, streaming APIs (SSE), asynchronous AWS pipelines, document processing, and responsive React UIs. Collaborate with product, design, and core platform teams to iterate and ship production-ready, low-latency AI experiences.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

As an AI Product Engineer at Puzzle Cats, you will develop and enhance AI-driven mobile game experiences, collaborating across teams to rapidly prototype, ship, and iterate on features that can quickly reach millions of players.

Design, build, and operate secure, scalable AI platform capabilities (IaC, Kubernetes, CI/CD, observability, IAM) to enable enterprise AI delivery. Create reusable services (model/API gateways, inference routing, vector/retrieval), enforce governance and SLOs, optimize cost/performance, and mentor engineers while partnering with security and architecture stakeholders.

Design and build secure, automated developer platforms and CI/CD pipelines for regulated enterprises. Implement software supply chain security, policy-as-code, governance automation, deployment strategies, observability, and reusable platform templates. Lead architecture, mentor engineers, and collaborate with security, risk, and operations stakeholders to enable compliant AI-assisted development.

Drive outbound pipeline and qualify opportunities for an AI platform in B2B insurance. Run discovery calls, refine ICP and messaging with founders, and optimize prospecting using tools like Clay and Sales Navigator.

Design, prototype, and productize scalable machine learning solutions using LLMs. Collaborate with clients and teammates to build production-ready systems, rapidly iterate prototypes, apply statistics and ML, and implement tooling and infrastructure for deployment and model-driven features.

Analyze and redesign business processes, identify automation opportunities, and build low-code RPA workflows (UiPath, Power Automate). Collaborate with stakeholders and developers for integrations, document designs and benefits, and provide occasional after-hours support.

Lead design and delivery of production-grade AI/LLM platforms and assets to automate controls across Claims, Underwriting, Finance, and Technology. Build scalable ML pipelines, inference and serving, RAG and agentic solutions, ensure security/privacy/compliance, optimize cost and performance, and enable regional teams to operationalize AI capabilities.

The AI Engineer will design and develop scalable AI solutions for finance, ensuring compliance and collaboration across teams. Responsibilities include coding, model lifecycle management, automation, and technical mentoring.

Build, deploy, and support AI agents and intelligent workflows on Microsoft Azure and other enterprise AI platforms. Design orchestration, integrate LLMs, implement APIs and RAG/vector DBs, ensure responsible-AI guardrails, collaborate across product and operations, and monitor production AI workflows.

Work on building production-ready agentic AI applications and the Continuum platform: agent orchestration, model routing, persistent memory, tool integrations, retrieval pipelines, guardrails, evaluation, observability, and backend/APIs. Write well-tested Python code and contribute to open-source examples and documentation.

Lead design, build, and integration of production-grade AI systems into the marketing stack to drive personalization, automation, and measurable pipeline growth. Own martech strategy, governance, integrations (Marketo, Salesforce, GA4, CMS, DAM), LLM-enabled agents and vector retrieval, ML forecasting, performance monitoring, and cross-functional alignment with IT, Legal, Security, and Analytics.

Lead hands-on design, build, and production of LLM-powered applications and agent workflows. Own end-to-end delivery: backend APIs, integrations, orchestration, deployment, monitoring, and iteration. Translate stakeholder needs into practical, scalable architectures, embed AI into enterprise workflows, and ensure secure, observable, compliant AI operations while contributing shared libraries and engineering patterns.

Design, build, and operate production-grade AI automations and integrations across marketing, operations, creative, and logistics. Develop AI-agent architectures and MCP servers, connect APIs and platforms, write Python/JavaScript, evaluate vendor tools, ensure security and reliability, and drive adoption via training and documentation.

Lead the AI Platform product vision, roadmap, and end-to-end user experience. Drive adoption through data-driven decisions, cross-functional alignment, global launches, post-launch optimization, and continuous improvement while navigating legal and regulatory constraints.

Lead strategy and execution for agentic AI product experiences across the DFIR portfolio. Define roadmaps, ship reliable and governed agentic features, drive adoption and growth, instrument metrics and experiments, run continuous discovery, and collaborate cross-functionally to ensure trustworthy, scalable AI-native products.

Lead end-to-end client engagements to scope, prioritize, and deliver AI-native products and agentic solutions. Run greenfield builds, legacy platform rebuilds, and client enablement; write PRDs, set guardrails, partner with engineers, manage stakeholders, and measure ROI.

Lead the Compass agent product as the technical and science owner: design and ship agentic/LLM-powered features end-to-end, set evaluation and experiment frameworks, build data-driven models and tooling, and collaborate across backend, data, and frontend to raise agent quality and enable safe action-taking behaviors.