Top AI Jobs in Toronto

As Technical Co-Founder & CTO you'll build and harden an LLM-powered, voice-enabled AI Front Desk for lawyers: own architecture, reliability, observability, delivery infrastructure, unit economics, roadmap, customer-facing technical representation, and recruit the founding engineering team to scale a managed AI staffing platform.

Lead LucentAI as Co-Founder & CEO: define strategy, run customer development and pilots with insurance-defence firms, partner on product/engineering to ship V1, raise seed capital, and recruit and lead the founding team. Equity-only founder role with venture-studio support.

Co-founder and technical lead responsible for building an explainable, audit-ready AI compliance platform: define architecture and security (SOC 2, tenant isolation, encryption, audit logging), ship LLM-powered production features (marketing review, surveillance, trade oversight), own model training and explainability layers, recruit and lead engineering/AI team, engage customers and investors, and drive product, technical, and go-to-market outcomes to reach early revenue targets.

Lead and operate an AI-native recruiting venture: set strategy, run pilot placements, partner on product development, raise seed capital, and hire and lead the founding team while leveraging FutureSight's studio resources.

Lead Weli AI as Co-Founder & CEO: define strategy, lead pilots with RIAs, convert pilots to paid customers, ship V1 with FutureSight's product and engineering team, raise seed capital, and recruit and lead the founding team while establishing company culture and go-to-market motion.

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.

Lead product and strategy operations for a large engineering department: own OKR/QBR cycles, roadmaps, weekly execution alignment, centralize artifacts, transform workflows to AI-native, coordinate cross-functional intake, flag risks, and drive high-priority cross-functional projects from inception to launch.

Design, build, and deploy production-grade AI-powered backend systems. Integrate LLMs, vector databases, and task-specific ML models into scalable, low-latency architectures. Implement background processing, performance tuning, testing, monitoring, and error reporting. Debug cross-layer issues and collaborate with product and frontend teams to deliver reliable AI-driven features.

The Principal Business Consultant, AI will lead projects, consult with clients on AI solutions, and improve business processes, ensuring optimal software adoption and utilization.

Lead Omnex as Co-Founder & CEO: set strategy, run customer development and pilots, partner on product and engineering, lead fundraising, recruit and build the founding team, and establish company culture.

Lead and operate an AI-native content intelligence and publishing startup: set strategy, validate customers, guide product development with FutureSight's studio, raise seed capital, recruit a founding team, and build go-to-market and growth motions for creators, teams, and enterprises.

Lead a venture-building AI clinical documentation compliance company: set strategy, run customer pilots and GTM, partner on product development, raise seed capital, recruit and lead the founding team, and establish company culture.

Lead Crossfoot as Co-Founder & CEO: define strategy, run customer development and pilots with CPA firms, partner on product development, raise seed capital, and recruit and lead the founding team while establishing company culture.

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.

Lead development of agentic AI features and shared platform components for a multi-tenant SaaS finance platform. Design LLM orchestration, CI/CD, deployment pipelines, observability, scalable background processing, ingestion and search systems, and PostgreSQL optimization. Drive technical vision, mentor engineers, and deliver production-grade, secure, and observable AI-first solutions end-to-end.

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.