Top AI Jobs in Toronto

Build AI-powered features from prototype through production, design and run evaluations, maintain data and inference pipelines, and improve model accuracy, latency, and cost. The role involves experimenting with LLMs, prompting, fine-tuning, retrieval approaches, and vector databases, while collaborating closely with teammates and owning work end-to-end.

Conduct applied research on foundation models for real-time fraud detection using large-scale behavioral and sequential data. Design experiments, benchmarks, holdouts, and monitoring systems; develop models from data preparation through deployment; and optimize training and inference efficiency. Collaborate with engineering, client-facing teams, legal, compliance, and customers to productionize models and establish explainability, governance, and risk documentation.

Build and deploy AI agents that automate internal business processes. Responsibilities include authoring reusable agent skills, integrating systems through MCP, creating evaluation sets, deploying production workflows with permissions and audit trails, and partnering with data engineers. The role requires translating business processes into reliable agent workflows, supporting deployed agents, and maintaining operational quality using Python, SQL, Git, CI, and containers.

Coordinates the AI Accountability Network’s newsletters, training programs, grants, fellowships, payments, events, communications, and community initiatives. Manages editorial workflows, contractors, applications, logistics, websites, listservs, dashboards, metrics, and stakeholder communications. Supports grant administration, funder reporting, travel, curriculum development, and website publishing while improving processes and tracking program impact.

Owns enterprise AI architecture across AWS, Azure, Microsoft 365, and SaaS platforms. Defines target-state architecture, standards, guardrails, and governance; aligns engineering teams and senior stakeholders; oversees AI platform capabilities, Microsoft AI surfaces, security, DevOps, data, responsible AI, and regulatory compliance. Evaluates emerging AI technologies, supports architecture governance, mentors senior architects and engineers, and ensures production systems generate model-risk and regulatory evidence.

Owns the design, development, and production deployment of AI-enabled software across diverse legacy and cloud environments. Responsibilities include building APIs, integrations, data pipelines, retrieval systems, user interfaces, and modernization upgrades; integrating LLM capabilities with SaaS, ERP, CRM, and on-premise systems; optimizing reliability, accuracy, latency, and cost; and creating reusable AI patterns, playbooks, and training for business units.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Join Welo Data’s Canadian French contributor network for potential remote AI projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with compensation and details provided before each assignment.

Join Welo Data’s French AI Trainers Network as a flexible contributor supporting annotation, evaluation, and prompt creation projects. Contributors may label text, images, or audio; assess search results, advertisements, and chatbot responses; create prompts for large language models; and ensure cultural and linguistic accuracy. This is a talent community rather than an active opening, with project details and compensation provided when assignments become available.

Build and own end-to-end AI-enabled internal tools and workflows for game content production. Design data models, backend services, APIs, orchestration, human-in-the-loop review, and UIs. Implement reliable, observable systems with approval gates, retries, audit trails, and staged processing. Partner with cross-functional teams to turn ambiguous goals into production-ready solutions and maintain high quality, maintainability, and engineering standards.

Leads and mentors data engineering teams while designing, deploying, and operating scalable data pipelines and platforms supporting analytics, AI/ML, and commercial use cases. Provides architectural leadership across cloud-native platforms, distributed processing, orchestration, data integration, and streaming architectures. Manages delivery priorities, stakeholder requirements, operational excellence, incident response, standards, and continuous improvement across enterprise data engineering initiatives.

Conduct research and design AI data-security and privacy architectures for endpoint devices. Prototype and build privacy-preserving defenses, analyze emerging attack vectors and regulations, and collaborate with global labs and product teams to transition research into production-grade endpoint security solutions.

Research and analyze state-of-the-art AI model architectures and training/inference workloads; map workloads to hardware requirements; develop performance models and deployment strategies; profile hardware/software, evaluate algorithm-hardware tradeoffs, and provide quantitative recommendations for next-generation AI accelerators.

Build backend infrastructure and APIs to integrate Lyft with emerging AI ecosystems and devices. Design scalable distributed systems, mentor engineers, drive high-impact projects, write and review maintainable code, participate in on-call rotation, and collaborate cross-functionally to deliver production-grade AI-enabled features.

Lead the end-to-end product experience across acquisition, onboarding, avatar selection, chat, media generation, and retention. Drive conversion, retention, and LTV through hypotheses, prioritization, A/B testing, and data-informed design. Establish the product’s design direction and design system, lead a multidisciplinary design team, and partner with product, analytics, marketing, engineering, research, and support.

The Sr. Machine Learning Engineer will design and deploy scalable ML systems, collaborate across teams, and ensure production readiness of ML models using modern frameworks and cloud services.

Lead execution of an enterprise AI program: govern a portfolio of cross-functional AI initiatives, build org-wide AI enablement and infrastructure, coordinate stakeholders and vendors, report program health to C-suite, and coach departments through AI adoption.

The Co-op Engineer will analyze source code, implement new features on demo apps for Android or HarmonyOS, and deploy AI models on Huawei devices, collaborating across teams.

The Senior Forward Deployed Engineer will deliver technical solutions using AI, engage with customers, and lead project lifecycles while mentoring junior engineers.

Design, build, and deploy AI-powered planning solutions on Pigment. Translate business problems into scalable models and custom AI agents, integrate data via APIs and pipelines, prototype agentic workflows, train users, drive adoption, and relay customer feedback to Product and Engineering to scale repeatable deployment patterns.

Lead design and delivery of AI agent solutions that convert documents into structured data for Veeva Quality Cloud. Define data flows, orchestration, model integration, and prompts; collaborate with engineering and UX to automate processes, evaluate AI agents, and present features to stakeholders.

Lead design and delivery of AI Agent solutions for Veeva QualityDocs and Training. Define data flows, orchestration, prompts, and model integration; collaborate with engineering and UX; evaluate AI agents and present capabilities to stakeholders.

Sales Specialist responsible for leading Data Center and Routing network sales, building pipeline, collaborating with Account Managers, and establishing consultative relationships with enterprise clients.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.

Leads AI-powered content strategy, governance, information architecture, conversational design, and content ecosystem development for enterprise clients. Defines taxonomies, dialogue flows, guardrails, golden queries, AI content pipelines, and human-review frameworks. Partners with design, product, and engineering teams to deploy and optimize chatbot, voice, and agentic experiences. Serves as a senior client advisor, leads multidisciplinary engagements, and supports account growth and proposals.