Top AI Jobs in Toronto

Build AI-native automation systems, developer tooling, and high-scale data ingestion for sportsbook odds and market feeds. Create AI workflows/agents that generate and validate code, maintain data feeds, automate debugging/testing/deployments, and monitor failures with automated remediation. Focus on internal automation, LLM orchestration, and distributed backend pipelines to accelerate the engineering team.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, explanations, and reasoning. Provide clear, opinionated feedback, distinguish quality levels, and help define standards for excellent AI-assisted developer interactions.

Own full B2B sales cycle to drive enterprise and API adoption of Viggle's video, 3D, mocap, and text-to-motion products in the US gaming market. Prospect, demo, negotiate, and close deals with studios and platforms; coordinate integrations and POCs with technical teams; manage pipeline, CRM, forecasts, contracting, and market feedback; represent the company at industry events.

Serve as the bridge between client problems and automation/AI solutions: lead discovery workshops, produce PDDs and agent specs, design prompts and evaluation frameworks, scope solutions, coordinate UAT and training, and support delivery across 2–3 engagements concurrently.

As an AI/ML DevOps Engineer, you will operationalize AI solutions, build CI/CD pipelines, and ensure production system reliability. Collaborate with data scientists and engineers to deliver AI capabilities and maintain strong operational practices.

Join a cross-functional team to develop data products, write efficient code, influence AI roadmap, and collaborate with diverse professionals.

The role involves designing and developing front-end and back-end components, mentoring team members, ensuring system efficiency, and optimizing platform performance in a collaborative team environment.

Design and implement scalable AI agent pipelines and runtimes, build automated code-generation and self-debugging loops, create advanced agent memory systems, develop automated evaluation frameworks for agent accuracy and latency, and translate cutting-edge agent research into production-grade software libraries.

Conduct research on safe, reliable embodied AI for robotic manipulation and autonomous decision-making. Develop and evaluate reinforcement learning, imitation learning, and learning-from-demonstration methods; leverage negative data, human feedback, and corrective demonstrations; design and train Vision-Language-Action and Vision-Language models; implement and test models in simulation and on real robotic platforms; collaborate to translate research into scalable prototypes and publish results.

Lead QA and validation for AI/ML systems in a GxP/GMP environment. Collaborate with data science, IT and business to design, validate, implement, and operate AI solutions under CSV/CSA. Author validation documentation, mitigate model and data risks, align stakeholders on compliant strategies, and drive continuous improvement and capability building in digital validation and model governance.

Own reliability, observability, and security for AI/ML platforms (data processing, workspaces, labeling, model serving). Build IaC and automation, define SLOs/error budgets, run incident response and DR exercises, implement security controls, mentor engineers, and optimize cost, capacity, and operational standards across cloud environments.

As an AI Performance Architect, you will analyze and optimize AI workloads on Tensix architecture, linking architecture with software and hardware to enhance efficiency and scalability.

The role involves creating reference drivers for AI-IP, collaborating with teams, optimizing performance, and supporting customer-specific systems in a hybrid work environment.

Lead rapid concept-to-production work using AI design tools and modern full-stack frameworks. Build accessible, performant frontends and robust backends, implement APIs, auth, caching, and databases, and collaborate with designers and AI builders to maintain design systems and deliver production-ready applications.

Lead the finance workstream for S/4HANA implementation and stabilization, design reporting and data models, drive AI/automation and Power BI/Fabric reporting, coordinate UAT and adoption, strengthen controls, and deliver scalable finance reporting and integrations across systems.

Lead the rollout and adoption of enterprise AI assistants across office and field teams. Plan phased deployments, map workflows to identify friction, build concise job-specific enablement in Docebo, intake and triage requests, create small automation solutions, instrument and report usage metrics, and influence cross-functional stakeholders to govern and activate appropriate AI features.

Join as a founding GTM team member to build and qualify pipeline, manage inbound chats/emails, run outreach automations, re-engage sign-ups, track CRM and metrics, and attend events to generate high-value relationships with developers, startups, and enterprises in the open AI ecosystem.

Full-cycle AE responsible for sourcing, qualifying, negotiating, and closing mid-market and enterprise AI cloud deals. Build technical relationships with CTOs and ML engineers, run demos, craft proposals, manage CRM (HubSpot), and help define GTM and expansion motions while mentoring BDRs.

Build and maintain an agentic platform that transforms high-stakes medical and legal casework into structured context. Work across the full stack: agent architectures, eval frameworks, data ingestion/indexing, and user-facing tooling. Ship workflows, design reliability-focused evals and guardrails, iterate with user feedback, and set patterns for AI-native development in a healthcare context.

Sell AI services and SaaS to new and existing B2B clients across industries, build pipeline, manage accounts, record CRM data, present complex proposals, and explain AI regulations and standards (e.g., 42001, 42006). Maintain client relationships and ensure timely delivery and retention.

Lead development of the application and cognitive layer for humanoid robots: implement behavior trees and intent-to-action orchestration, build voice applications and multi-modal fusion, integrate robotics capabilities and edge/cloud AI services, and ensure production readiness with CI/CD, telemetry, and monitoring.

Build and maintain responsive, production-scale frontend web applications for an AI Receptionist product. Design UI for configuring conversational agents, integrate with backend services (APIs, WebSockets, streaming), optimize performance and accessibility, establish frontend architecture and best practices, and mentor engineers.

Own analysis and algorithmic solutions for ads delivery; train, deploy, and scale ML/optimization models into production; build logging, monitoring, and lineage; improve data pipelines; mentor data scientists; use agentic AI tools daily; collaborate with engineers to integrate models into ads systems and handle on-call production incidents.

Lead end-to-end delivery of an AI enablement program for SMEs: translate strategy into workplans, coordinate cross-functional teams and vendors, manage budgets and risks, maintain program systems and reporting, and ensure measurable participant outcomes.

Lead the roadmap, development, and rollout of Jerry.ai’s AI platform and customer-facing agent experiences. Partner with engineering and OpenAI on prompt strategies, evaluation frameworks, guardrails, voice models, and workflow automation. Translate AI and architecture concepts across the organization, establish platform standards, and improve answer quality, customer satisfaction, and automation through experimentation and data analysis.