Top AI Jobs in Toronto

Analyze credit risk and portfolio data, prepare recurring and ad hoc reports, support analytics automation, contribute to data lifecycle activities, document requirements, and explore generative AI tools for risk-management insights.

Design and deliver data analytics and AI solutions for Capital Markets. Apply statistical and machine learning models, build AI agents and dashboards, produce insights for senior management, and collaborate with data engineers and business teams to ensure governance and integration.

Build and deploy generative AI agents and workflow automations for wealth management teams. The role includes discovering use cases, designing and testing solutions, optimizing model costs, delivering training, supporting adoption, managing AI champions, addressing risk and privacy requirements, measuring business outcomes, and collaborating with vendors. The specialist bridges business and technology needs while ensuring solutions comply with financial industry regulations.

Manage a cross-functional engineering team building GitLab Duo Chat's AI-powered features. Define requirements with product and UX, contribute to architecture for scalable agentic AI, drive delivery and roadmap execution, improve testing, observability, performance, and coach engineers to foster an inclusive, high-performing team.

Leads the design, development, deployment, and governance of enterprise AI and generative AI solutions. Builds LLM applications, AI agents, RAG systems, intelligent assistants, and scalable AI architectures using Azure technologies. Integrates AI with enterprise data and applications, establishes governance and responsible AI practices, and implements MLOps/LLMOps, monitoring, evaluation, and lifecycle management. Partners with clinical, research, business, and technical stakeholders to deliver AI-enabled transformation initiatives.

Research, design, and prototype next-generation data systems for AI applications (LLMs, agents, RAG). Build scalable architectures for vector search, semantic retrieval, knowledge management, and AI data lifecycle. Evaluate state-of-the-art vector/graph databases and distributed storage, develop proofs-of-concept and production components, and publish patents and papers.

The role involves developing and deploying generative AI applications using Microsoft Azure AI stack, focusing on LLMs, agent-based workflows, and scalable AI infrastructure.

Lead architecture and implementation of AI-first backend and orchestration systems for Audit Suite. Drive scalable, reliable AI workflows (LLM integration, retrieval, vector stores), mentor engineers, set technical direction, and collaborate across product, design, and ML to deliver auditable, high-throughput document-centric solutions.

Design, deploy, and operate production-grade Generative AI agents and GenAI workflows for clinical trial optimization. Build scalable cloud infrastructure, automate via IaC and CI/CD, integrate LLM routing and monitoring (latency, token usage, hallucination detection), and collaborate with AI engineers and data scientists to move models from research to reliable production services.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Design, build, and scale production GenAI and agentic solutions (conversational AI, RAG, agent workflows) across Databricks and AWS. Integrate AI with enterprise data, APIs, and tools securely, apply evaluation and observability, and partner with platform teams to deploy governed, measurable AI experiences.

Build and standardize ML and AI platform patterns to move models from experimentation to production. Implement MLOps/LLMOps practices (Databricks, AWS, MLflow), deployment templates, monitoring, evaluation, governance, and secure serving. Partner with Data Engineering and Governance to version, monitor, and govern models, prompts, agents, and data products while managing cost, performance, reliability, and operational risk.

As an ESG & CSR Responsible AI Intern, you'll develop AI ethics principles, collaborate with teams, conduct audits and promote transparency in AI practices.

Leads requirements and backlog management for AI and LLM products, coordinates deployments across environments, facilitates release readiness and go/no-go decisions, supports production verification and incident triage, and drives agile ceremonies. Serves as the liaison among AI Engineering, DevOps, QA, Product, architects, and stakeholders while translating complex technical and security requirements into actionable plans.

Design, develop, and deploy enterprise-scale financial technology applications using Java, Spring, Kafka, Angular, and React.js. Build AI-enabled features, manage MongoDB and Oracle data stores, write tested and maintainable code, and contribute to Agile and DevOps practices with Kubernetes. Collaborate with front-office teams, mentor engineers, conduct code reviews, and provide technical guidance to stakeholders.

Design, build, and deploy enterprise-scale agentic AI and generative AI solutions for banking. Develop full-stack LLM applications, integrate multiple model providers, establish evaluation and feedback systems, and research emerging autonomous AI technologies. The role emphasizes rapid MVP delivery, production reliability, security, and scalable architectures. It also requires cross-functional collaboration, technical mentorship, and alignment with regulated financial-services requirements.

Lead end-to-end delivery of AI solutions for Risk, Legal, Procurement, and Compliance, focusing on fraud/financial-crime detection. Shape roadmaps, manage multidisciplinary teams, perform hands-on AI engineering and prototyping, ensure security, governance, model risk and responsible AI, and drive enterprise integration and adoption to realize measurable business outcomes.

Design and deliver production-ready machine learning, GenAI, and agentic solutions for actuarial and enterprise business use cases. Responsibilities include predictive modeling, forecasting, segmentation, anomaly detection, retrieval-based GenAI, evaluation, monitoring, governance documentation, deployment collaboration, UAT support, and mentoring junior scientists.

Leads governance and portfolio enablement for Canadian Personal Banking AI and analytics initiatives. Oversees lifecycle progression, prioritization, risk and control coordination, governance readiness, and executive reporting. Partners with business, technology, model risk, compliance, cybersecurity, and data governance teams to operationalize AI standards. Develops Power BI dashboards, delivers training and communications, supports change management, and promotes responsible AI-assisted productivity improvements.

Architect and deploy scalable machine learning and generative AI systems, including LLM, RAG, inference, evaluation, and LLMOps pipelines. Develop prompting, retrieval workflows, guardrails, schema validation, and production evaluation systems focused on accuracy, safety, reliability, and cost. Lead technical decisions, collaborate with product and engineering teams, drive greenfield initiatives, and mentor engineers building secure AI-powered experiences.

Drives revenue growth through Microsoft’s partner ecosystem by developing co-sell strategies, managing cloud solution area sales plans, accelerating partner pipelines, forecasting revenue, and coordinating internal and external stakeholders. Advises partners on solution plays, investments, deal execution, security offerings, and commercial performance while ensuring targets, compliance, and predictable business rhythms are achieved.

Design and build scalable AWS cloud, Big Data, and machine-learning infrastructure for AI-powered Workforce Management. Lead distributed data pipeline and workflow-orchestration projects from architecture through production, improve reliability, scalability, observability, security, and cost efficiency, and develop automated testing. Collaborate across engineering, data science, product, and platform teams while influencing architecture, owning major initiatives, reviewing designs and code, and mentoring junior engineers.

Design, build, and deploy GenAI and agent-based systems for Wealth Management. Lead end-to-end solution development, integrate AI with existing platforms, enforce MLOps, model governance, security, and regulatory compliance, and collaborate with cross-functional teams to drive production-grade, scalable AI products.

Develop and prototype AI applications for market and counterparty credit risk policy development and management. Collaborate with stakeholders to translate business problems into analytical solutions, conduct exploratory data analyses, identify actionable insights, and automate workflows. The role involves experimenting with LLMs, generative AI, prompt engineering, MCP integrations, machine learning, and deep analytics while communicating technical findings to non-technical stakeholders.

Design, develop, and deliver enterprise full-stack applications using Java, Spring Boot, and Angular. Provide technical leadership through architecture, code reviews, mentoring, and hands-on problem solving. Embed AI coding tools and establish team standards for prompt engineering and quality. Apply relational and NoSQL data design with Oracle and MongoDB. Support cloud-native engineering through Docker, OpenShift, Kubernetes, infrastructure as code, CI/CD, and modern software delivery practices.