Top AI Jobs in Toronto

Embed within the M&A team to identify, prioritize, prototype, and deploy AI and data-science solutions that speed deal sourcing, diligence, pricing, and integration. Architect the data foundation, translate between business and technical teams, build ROI-backed use cases, ship prototypes into production, drive adoption, and measure model performance, time saved, decision quality, and dollar impact across the transaction lifecycle.

As a Machine Learning Engineer, you will develop user experiences using LLMs and predictive analysis, deliver ML solutions, and ensure continuous improvement in AI products at scale.

Lead enterprise product vision and multi-year strategy for integrated product strategy and AI across ABC's portfolio. Build and manage a high-performing product and UX team, define AI-enabled platform and API-first capabilities, partner cross-functionally on go-to-market and technical decisions, and drive platform convergence, monetization, and measurable business outcomes.

Lead operationalization and coordination of Aviva Canada's AI Governance Framework: maintain AI inventory, coordinate model and non-model assessments, track remediation, prepare executive materials, define AI controls and metrics, support assurance and automation of governance processes, and drive stakeholder engagement for responsible AI adoption.

Build and scale production AI systems, APIs, data pipelines, retrieval systems, and model-serving infrastructure for consumer and clinician-facing health products. Integrate and fine-tune LLMs and other models, manage AI feature lifecycles from prototyping through monitoring, and ensure reliability and observability. Collaborate with engineering, product, and science teams. More senior candidates may lead AI architecture, roadmap development, and mentoring.

Own the AI and data product roadmap, prioritize and sequence development with engineering and founders, ship trustworthy AI experiences, drive adoption, define feature success metrics, evaluate outcomes, and manage the analytics foundation. The role requires technical fluency in AI, prior software engineering experience, production AI product delivery, strong product judgment, and comfort working in a fast-paced, remote startup environment.

Design, build, and deploy scalable AI features using LLMs, knowledge graphs, and orchestration frameworks. Integrate models into enterprise products, optimize performance and latency, and manage model lifecycle, observability, and cloud-native deployments. Collaborate cross-functionally, lead engineering workstreams, and maintain high quality, secure production services.

Lead design and delivery of scalable, governed data ingestion and integration pipelines for structured, semi-structured, and multimodal data to enable AI, ML, and Generative AI. Partner with product, data science, architecture, risk, and operations to define requirements, ensure data quality, lineage, security, and observability, and evaluate new technologies. Mentor engineers, drive cross-functional initiatives, improve platform resilience, and measure ingestion and AI data effectiveness.

Design, build, and operate the AI platform powering conversational agents and large-scale document workflows. Own orchestration, retrieval, inference routing, observability, evaluation, and reliability for scalable, cost-sensitive LLM systems. Define APIs, data contracts, quality gates, and governance; optimize performance and cost; mentor engineers and lead technical decisions across distributed teams.

Lead a 20+ person Data & AI org owning strategy, platform, MLOps, applied AI productization, data governance, experimentation, and cross-functional partnership to drive AI-powered customer outcomes.

The Lead AI Forward Engineer designs AI solutions, guiding implementations for scalability and compliance, while mentoring teams and shaping technology strategy.

Lead enterprise AI architecture strategy and roadmap; design scalable AI, data, and integration architectures; establish reusable frameworks, standards, and governance; drive platform and technology selection; ensure security, compliance, and responsible AI; partner with business leaders and mentor teams to deliver enterprise-scale AI solutions.

The Senior AI/ML Engineer will develop AI systems for enterprise decision-making, focusing on LLMs and real-time data processing. Responsibilities include building detection systems, insight engines, evaluation frameworks, and collaborating with engineers to ensure robust AI product engineering.

Audits AI-generated support responses for accuracy, completeness, hallucinations, compliance, and customer trust. Governs knowledge sources, identifies content and workflow gaps, analyzes AI failures, builds performance dashboards, and partners with Content, Product, Engineering, and Support teams to improve AI training, guardrails, workflows, and resolution quality.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Enable strategic GSI and ecosystem partners on Workday HCM and agentic AI: develop functional playbooks, translate roadmap into partner-ready narratives, advise on competitive positioning and regulatory risks, support demo readiness, and act as a first-line functional expert for partner HCM teams.

Lead engineering execution and manage a senior team developing Thunderbolt, an open-source enterprise AI product. Establish scalable planning, code review, testing, release, documentation, and quality practices while contributing code and reviewing technical work. Partner with product and technical leadership to translate AI strategy into delivery plans, improve reliability and enterprise readiness, support customer-controlled architectures, and grow the engineering organization through hiring, coaching, and resource planning.

Own the generation side of Document Intelligence by designing prompts, schemas, validators, multimodal context assembly, and model-selection strategies. Build evaluation datasets and rubrics, monitor factuality and quality, identify regressions, and ship reliable LLM features into production. Work with video, PDF, audio, and image inputs to generate validated maintenance entities while balancing quality, cost, and latency.

Develop backend systems for 3D AutoReview, including geometry parsing, model analysis, quality detection, rule systems, scalable APIs, and AI-assisted review workflows. Collaborate with backend, frontend, product, and machine learning teams to deliver production features. Build extensible systems for custom 3D rules and reference data while experimenting with CAD, machine learning, and large language model technologies.

Design, architect, and deploy production-grade generative and agentic AI systems (RAG, knowledge graphs, multi-agent orchestration). Build context engineering, retrieval, observability, and governance for scalable, grounded AI solutions; integrate with cloud infrastructure, container platforms, and CI/CD; mentor engineers and ensure responsible AI practices.

Build and deploy LLM agents, Python automations, data workflows, dashboards, and root-cause analysis tools. Analyze telecommunications data with SQL to improve network quality, reduce fraud and billing errors, and optimize margins. Establish data architecture, governance, KPIs, and safe AI orchestration frameworks. Partner cross-functionally with Carrier Relations, Technology, Product, Supply Operations, and Finance to deliver scalable data products and communicate actionable insights.

The Engineering Manager will lead the development of a scalable Search Platform, mentor teams, manage business outcomes, and enhance engineering efficiency while ensuring high-quality software delivery.

Manage a full-stack engineering team to integrate generative AI and agentic workflows into product experiences. Own frontend and backend delivery, shared data representations, API-first integration surfaces, developer tooling, operational reliability, and cross-functional collaboration to productionize safe, performant AI capabilities.

The role involves designing and implementing AI features, building APIs, participating in code reviews, and ensuring software scalability and reliability.

Build and maintain Geotab's internal ML and generative AI platform, including model management, logging/monitoring, and alerting. Enrich large-scale telematics data, integrate data pipelines with data scientists, and support internal teams with infrastructure, documentation, and production GenAI applications and agents.