Top AI Jobs in Toronto

Develop and deploy production machine learning solutions for retail personalization and loyalty offers. Build recommender and ranking models, large-scale PySpark feature pipelines, offline evaluation frameworks, ML platform infrastructure, and agentic AI systems. Operate cloud training workflows and analytical pipelines while improving reliability, scalability, and engineering standards through mentorship and code review.

Designs enterprise security architecture, governance, and risk frameworks for AI and machine learning systems. Responsibilities include establishing secure AI and MLOps architectures, mitigating AI-specific threats, implementing governance aligned with NIST and ISO standards, protecting AI data, ensuring privacy compliance, and advising executives. The role also mentors analysts and guides engineering teams on secure, ethical, and compliant AI deployment.

Designs, develops, and deploys production-grade AI and generative AI solutions in enterprise cloud environments. Responsibilities include building NLP, LLM, predictive modeling, and intelligent-agent applications; integrating AWS Bedrock and SageMaker; designing APIs and microservices; managing model lifecycle, security, monitoring, and costs; and providing technical leadership, code reviews, and mentorship.

Designs and leads enterprise AI and generative AI solutions from use-case definition through production. Responsibilities include cloud-native architecture using AWS Bedrock and SageMaker, defining AI standards, selecting platforms and models, partnering with executives, ensuring governance and compliance, and mentoring delivery teams. The role also incorporates responsible AI, risk management, monitoring, lifecycle practices, LLM platforms, RAG, agent systems, and MLOps.

Lead enterprise AI adoption across business and technology teams by developing change strategies, enablement programs, communities of practice, playbooks, governance guidance, and adoption metrics. Embed AI into operational workflows, remove adoption barriers, track ROI and value realization, and partner with technical AI teams. The role requires strong change management, stakeholder engagement, AI literacy, and enterprise technology adoption experience.

Chief of Staff to the CEO responsible for driving company priorities, building the organization’s operating cadence, managing KPI reporting, leading cross-functional special projects, and increasing executive leverage. The role requires turning ambiguous problems into measurable plans, coordinating leaders and external partners, deploying AI effectively across operations, and improving organizational efficiency in a high-growth B2B SaaS environment.

Designs, builds, and operates secure, scalable cloud-native infrastructure for AI and generative AI workloads. Responsibilities include GPU and Kubernetes platforms, model serving, RAG infrastructure, APIs, microservices, networking, observability, CI/CD, infrastructure as code, security, compliance, and FinOps. The role defines AI cloud architecture and platform standards, leads solution design, operates systems using SRE practices, and mentors engineers. It requires extensive Azure or AWS experience, distributed cloud infrastructure expertise, and hands-on AI/ML platform engineering.

Leads the architecture, development, deployment, scalability, reliability, and operational excellence of enterprise AI and machine learning systems. Partners with researchers to productionize LLMs, agentic AI, and responsible AI capabilities; establishes MLOps and LLMOps practices; oversees observability, incident response, performance, security, and governance; and mentors engineers while providing senior technical leadership.

Own one or two client AI product engagements from kickoff through iteration. Translate ambiguous needs into requirements, backlogs, sprint plans, and evaluation criteria; manage timelines, scope, resources, and client communications; direct designers and engineers; ensure AI output quality; identify blockers; and develop repeatable AI-first delivery processes. The role requires client engagement experience, a track record of shipping AI products or features, strong communication, technical fluency, and independent ownership.

Leads qualitative and quantitative market research to uncover customer, market, competitive, and healthcare industry insights. Designs and manages surveys, focus groups, interviews, brand trackers, and complex research studies; analyzes data using statistical and AI-based tools; and delivers reports, dashboards, presentations, and recommendations. Collaborates with product, engineering, sales, marketing, and customer success teams, coordinates external research partners, maintains data quality, and manages multiple research projects and timelines.

Manage and grow business development teams responsible for generating qualified sales opportunities. Exceed sales targets, coach and develop team members, recruit for future needs, monitor KPIs, collaborate with sales leadership and business operations, improve processes, and align stakeholders across sales segments. The role requires strong sales leadership, communication, organization, and experience in fast-paced technology or SaaS environments.

Designs, builds, deploys, and supports production-grade AI applications for business stakeholders. Responsibilities include identifying AI opportunities, developing agents, Copilot solutions, automation workflows, and RAG systems; integrating enterprise data and applications; managing delivery through production; monitoring adoption and performance; and ensuring security, privacy, compliance, and responsible AI governance. The role also develops reusable engineering frameworks and advises leaders on AI strategy and implementation.

Leads AGF’s AI platform engineering strategy, architecture, governance, and operations. Owns platforms including Azure AI Foundry, Databricks, Copilot Studio, and Claude; establishes reference architectures, CI/CD, infrastructure as code, observability, security, cost controls, data governance, and lifecycle management. Builds shared components and developer tooling, guides AI engineers, evaluates emerging platforms, partners with risk and compliance teams, and develops a platform engineering team to deliver secure, production-grade AI capabilities.

Build and integrate AI agents, assistants, agentic workflows, APIs, MCP services, backend systems, secure data solutions, and front-end components for a public-sector AI platform. The role involves researching emerging AI technologies, integrating enterprise systems, collaborating in Agile teams, and independently driving secure, scalable technical delivery from high-level requirements to completion.

Lead enterprise AI platform deployment, adoption, governance, and measurement. Plan rollouts from pilots to enterprise scale, partner with Security/Legal/IT, configure identity and provisioning, build enablement programs, create adoption analytics and executive reporting, and remove adoption barriers to maximize business value.

The Engineering Manager leads Data Engineering teams, developing scalable data solutions and collaborating with stakeholders to drive McCain's digital transformation.

Own product vision, strategy, roadmap, prioritization, and backlog for SaaS web and mobile products. Develop deep customer workflow expertise, translate insights into features, lead cross-functional agile delivery, and establish product KPIs. Drive AI-enabled capabilities by prototyping with LLMs, evaluating models and vendors, creating prompts, agents, and evaluations, and applying responsible AI practices. Lead product launches, demonstrations, planning, stakeholder alignment, and continuous improvement.

Leads AI transformation consulting from business development through delivery. Owns pipeline, executive sales, pricing, proposals, client engagements, AI working sessions, prototypes, change management, account growth, partner delivery, and commercial outcomes. The role combines consultative selling with hands-on AI capability building, executive communication, and enterprise transformation leadership across multiple accounts.

Own the AI-enabled Customer Care analytics architecture and roadmap while personally analyzing data to guide product and operational decisions. Build BigQuery-based AI workflows, reusable metric layers, dashboards, reports, and anomaly detection capabilities. Translate complex data into actionable narratives, improve data trust, support ROI modeling, and facilitate analytical discussions with Product, Operations, Data Science, Engineering, and senior leadership.

Conduct applied AI/ML research focused on time-series forecasting and generative AI for supply chain planning. Review literature, formulate hypotheses, design reproducible experiments, develop and evaluate models, analyze results, and communicate findings. Build research prototypes and collaborate with researchers, engineers, data scientists, and domain experts to assess product potential. Contribute to technical reports, demonstrations, publications, and patents under mentorship.

Monitors and improves the performance, reliability, and adoption of AI-powered systems. Analyzes operational metrics, develops dashboards and reports, identifies bottlenecks, supports incident resolution, documents procedures, and recommends process improvements. The role collaborates with AI engineers, product teams, business stakeholders, and operations leaders while supporting governance, compliance, risk mitigation, and continuous improvement initiatives.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product requirements, priorities, success metrics, and business outcomes while coordinating engineering, AI, executives, and business stakeholders. Evaluates emerging AI technologies, supports responsible AI governance, manages feedback and adoption, and drives continuous product improvement.

Own AI product backlogs, priorities, user stories, acceptance criteria, and delivery outcomes. Translate business needs into product requirements, collaborate with technical teams on solution design, facilitate stakeholder communication, support Agile delivery, and monitor product performance and adoption. The role also identifies AI-driven improvements, supports product releases, drives continuous improvement, and promotes responsible AI practices.

The AI Security Analyst assesses and mitigates risks across AI systems, machine learning platforms, and Generative AI applications. Responsibilities include threat modeling, vulnerability identification, security monitoring, incident response, root cause analysis, access control, policy development, compliance support, audit participation, and secure design reviews. The role partners with engineering, infrastructure, governance, and business teams to improve AI security capabilities and promote responsible AI adoption.

Translates business needs into AI solution requirements, assesses AI and automation opportunities, develops business cases, and supports solution design and delivery. The role facilitates stakeholder collaboration, testing, implementation, change management, reporting, benefits tracking, and continuous improvement across AI initiatives.