Top AI Jobs in Toronto

Manage and grow a portfolio of healthcare customers through adoption, expansion, renewal, and AI product deployment. Build executive and clinical relationships, identify workflow opportunities, develop ROI-focused business cases, coordinate implementations with Clinical Engineers, monitor account health, forecast renewals, and troubleshoot common product issues. The role is quota-carrying and requires strong commercial judgment, product fluency, technical communication, and the ability to translate customer needs into measurable clinical and operational value.

Own the SafeSend product portfolio and lead its evolution into an AI-driven Gather & Deliver experience. Responsibilities include converging SafeSend and TaxCaddy, developing reusable workflow components, expanding APIs and integrations, enabling agent orchestration through CoCounsel, and delivering reliable tax-season performance. The role requires platform product leadership, customer migration expertise, AI product fluency, systems thinking, stakeholder management, and team-building skills.

Leads fraud analytics, customer risk scoring, fraud technology, intelligence, risk assessments, and prevention strategy across banking products and channels. Develops detection models, monitoring frameworks, dashboards, and executive reporting; manages vendors and governance activities; partners with risk, compliance, technology, data, and operations teams; and builds a high-performing fraud analytics and risk management team.

Leads CIBC’s Insurance Decision Science team, setting technical standards for actuarial, statistical, data science, and AI-enabled methods. Oversees analytics supporting insurance pricing, profitability, growth, forecasting, portfolio performance, and strategic decisions. Builds capabilities, challenges assumptions, advises executives, manages talent strategy, and develops relationships across insurance, retail banking, technology, and analytics teams.

Design, build, deploy, and improve secure, resilient cloud-native applications using .NET, React, Java, Kubernetes, Azure, and DevSecOps practices. Translate requirements into technical solutions, validate architecture, implement quality and security controls, and use AI-assisted engineering tools responsibly. Mentor engineers through pairing, code reviews, and technical guidance while collaborating with product, design, and agile stakeholders. Prototype new functionality, maintain observability and reliability, and support continuous learning and improvement.

Analyze the customer lifecycle across activation, engagement, retention, expansion, and renewal for a product-led SaaS business. Partner with Customer Success, Product, Growth, RevOps, and Data Engineering to develop analytical frameworks, identify growth opportunities, evaluate experiments, and provide actionable recommendations to senior leaders. Build data foundations and standards supporting digital success, AI, automation, forecasting, anomaly detection, and generative BI.

Conduct independent research on machine learning, knowledge representation, formal methods, AI agents, and trustworthy AI systems. Develop algorithms, models, prototypes, benchmarks, and rigorous experiments for enterprise-scale applications. Collaborate with scientists, engineers, and domain experts to translate operational constraints into deployable research systems. Evaluate reliability, robustness, privacy, interpretability, latency, cost, and process outcomes, while communicating findings through publications and internal or external presentations.

Define and scale CoLab’s supplier ecosystem go-to-market strategy across OEMs, Tier 1 suppliers, and Tier 2 suppliers. Package and price the supplier motion, map supply networks, develop value cases, enable prospecting and customer success teams, and support strategic deals. Lead executive workshops, coach account teams, identify supplier-sourced opportunities, track activation and pipeline metrics, and translate market signals into product feedback. The role is remote-friendly in Eastern or Central time zones and includes frequent customer travel.

Own the discovery, direction, delivery, adoption, and continuous improvement of AI- and analytics-powered products for Commercial Banking. Translate business and user needs into product requirements, user stories, acceptance criteria, prioritized backlogs, and roadmaps. Partner with business leaders, data scientists, engineers, architects, and product teams to deliver measurable value, improve workflows, champion user experience, and align competing priorities.

Owns product vision and roadmap for Mastercard’s identity and payments data products. Responsibilities include prioritizing product development, creating data-backed business cases, aligning cross-functional stakeholders, defining requirements, managing risks, tracking delivery, representing customer needs, and developing future commercial opportunities. The role also involves mentoring product managers, managing globally distributed teams, and communicating with executive stakeholders.

Leads the design, deployment, and scaling of AI-assisted software development capabilities, including AI CI/CD, automation, agentic systems, and platform integrations. Provides technical direction and mentorship across engineering squads, establishes reusable engineering patterns, manages AI technology adoption, and ensures secure, compliant operation in a regulated financial environment. Partners with leadership on architecture and trade-offs while driving monitoring, observability, safety guardrails, and production scalability.

Supports Questrade’s enterprise AML and ATF program through virtual currency transaction monitoring, unusual activity reviews, case research, suspicious transaction reporting, and compliance investigations. The role maintains AML policy expertise, collaborates with internal business units, recommends actions to leadership, and identifies AI and automation opportunities to improve risk detection and oversight.

Lead technical strategy and pre-sales engagement for large enterprise accounts, guiding customers through Databricks adoption and data-driven transformation. Build executive relationships, present technical value propositions, coordinate virtual teams, develop technical champions, align account strategies, and mentor colleagues. The role requires expertise in big data, data science, cloud, ML and AI use cases, plus production programming experience in Python, R, Scala, or Java.

Leads strategic pricing and monetization across sales-driven channels, managing pricing partners and advising on complex commercial deals. Develops pricing frameworks, approval processes, deal governance, and scalable tools using automation, AI, and data. Partners with Sales, Product, Engineering, Legal, Risk, and Revenue Operations to improve deal economics, profitability, pricing performance, packaging, and portfolio-level monetization strategy.

Leads and mentors a team of applied machine learning scientists developing predictive AI for regression, classification, and anomaly detection. Provides technical and strategic leadership, designs enterprise-scale AI/ML systems, translates business objectives into scalable solutions, collaborates with engineering and research teams, and ensures governance and regulatory compliance across Canadian Personal Banking.

Develop and validate predictive credit risk models using large retail lending datasets, advanced statistical methods, and machine learning. Build dashboards highlighting expected credit losses and risk drivers, support model validation and compliance, and provide analytical insights to retail risk, finance, and product stakeholders. The role also involves data mining, portfolio segmentation, model documentation, and decision support for lending growth, profitability, and risk management.

Own the product proposition, user experience, positioning, and commercial strategy for RLDatix Intelligence. Translate unified data platform capabilities into intelligence products, benchmarking solutions, AI-enabled interventions, and conversational experiences for healthcare users. Define persona-based workflows, partner with product and technical leaders, identify market opportunities, and develop new data-driven offerings across the RLD360 portfolio.

Analyzes, monitors, and reports counterparty credit risk exposures against approved limits. Investigates and escalates credit excesses, ensures accurate risk measurement and reporting, supports policy and infrastructure changes, maintains internal controls, and performs ad hoc exposure analysis. The role partners with credit risk, market risk, front-office, audit, technology, and other stakeholders while supporting process improvements and operational risk management.

Leads strategic projects, process improvement, technology initiatives, and change management for the Markets department. Owns the firm’s CRM platform and Markets technology roadmap, oversees vendors and a small team, and drives governance, reporting, budgeting, user adoption, and operational efficiency. Partners with IT, Finance, Procurement, executives, and business stakeholders to implement technology solutions, optimize workflows, and leverage automation and AI.

Leads enterprise AI deployments across contact center and operational functions, managing requirements, vendors, configuration, testing, rollout, performance tracking, governance, and adoption. Coordinates cross-functional technology projects involving IT, Operations, Sales, Training, Field teams, and external partners. Defines KPIs, monitors AI performance, develops business cases, supports change management, and translates data and customer feedback into continuous improvements and new AI use cases.

Develop and operationalize cyber threat intelligence capabilities by collecting, analyzing, and disseminating actionable intelligence. Produce reports, advisories, alerts, briefings, and recommendations for technical and executive audiences. Support incident response and threat hunting, manage intelligence workflows and IOC lifecycles, conduct adversary and trend analysis, and build information-sharing partnerships. Apply AI and machine learning to improve collection, correlation, detection, and early warning while advising security teams on intelligence-driven resilience improvements.

Supports the design, development, and delivery of technology solutions for life sciences clients. Analyzes client requirements, develops solution proposals, performs qualitative and quantitative analyses, creates documentation, reports, and presentations, and recommends improvements. The role manages client follow-up and satisfaction, supports business growth, develops consulting and pharmaceutical market knowledge, and builds practice-specific expertise while collaborating with internal and global stakeholders.

Chief of Staff supporting the Chief AI Officer by managing organizational planning, operating rhythms, budgets, headcount, business reviews, cross-functional dependencies, and strategic initiatives. The role coordinates research, modeling, product, delivery, security, finance, and other teams to ensure priorities are aligned and programs ship effectively. It also leads special projects, facilitates executive communication, resolves ownership and trade-off issues, and represents the AI organization in high-stakes discussions.

Develop and operationalize cyber threat intelligence capabilities by collecting, analyzing, and disseminating actionable intelligence. Produce reports, advisories, alerts, and briefings; support incident response and threat hunting; manage intelligence processes, IOCs, and TIP governance; and improve intelligence-sharing workflows. Apply AI and machine learning to threat analysis, adversary profiling, IOC generation, and detection workflows while validating accuracy and responsible use. Advise security teams and senior stakeholders on emerging threats, business impact, and mitigation strategies.

Conducts cross-functional analysis, reporting, and process improvement for Trade Finance Operations. Acts as a subject matter expert on trade finance systems and processes, gathers requirements, develops functional documentation and use cases, supports development teams, and leads testing. Identifies control deficiencies and operational risks, implements corrective actions, manages high-risk issues, and provides analytics-driven recommendations to management. Collaborates with business, technology, and operations teams in an Agile environment.