Top AI Jobs in Toronto

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.

Design, build, and operate AI-driven detection engineering systems and orchestrated agent workflows to automatically generate, validate, optimize, and deploy cybersecurity detections. Focus on token-efficient LLM usage, deterministic pipelines (Python), CI/CD integration, context engineering, and embedding AI into SOC detection lifecycles.

Lead AI enablement and scalability for BMO InvestorLine: drive adoption of internal AI tools, build learning programs and knowledge ecosystems, design agentic operating model, partner cross-functionally to identify and scale practical AI use cases, develop cost-sharing and value attribution, and ensure responsible AI governance and measurable impact.

Provide expert legal and business research to attorneys and staff using advanced research tools; advise on research strategy; manage research workflow; develop training, onboarding, newsletters, and intranet content; evaluate and recommend online resources; support continuous learning and Research Services initiatives.

Lead product strategy and roadmap for AI-driven ordering, integrations, payments, and platform features. Run discovery, prototype, and ship features, design AI-assisted product operations, manage a small PM team, and own ERP integration strategy and customer-facing engagement to drive product outcomes and velocity.

Lead end-to-end development and deployment of AI/ML (including Agentic and LLM) solutions for enterprise-scale data reconciliation. Design models, manage pipelines and MLOps, analyze large structured/unstructured financial datasets, collaborate with engineering and business teams, maintain documentation, and deliver measurable impact across operations, risk, and finance.

Design, build, and deploy scalable AI/ML solutions: end-to-end pipelines, fine-tune LLMs, production model deployment with CI/CD, serve models via microservices/APIs, monitor model performance, collaborate with data/cloud teams, and prototype/evaluate new AI frameworks using Python, SQL, Spark and MLOps tools.

Serve as the bridge between People teams and engineering to architect AI-first HR/People solutions. Define roadmaps, design business processes, write requirements/user stories, and guide integrations across Workday, ATS, and boundary apps. Drive AI-enabled automation, responsible AI frameworks, ROI cases, data/privacy compliance, and support M&A People systems integration while influencing stakeholders and ensuring delivered solutions meet business objectives.

Lead the development of AI-powered solutions, drive technical strategy and innovation, mentor engineers, and ensure quality and operational excellence.

Build and maintain front-end features for an AI-driven contract management SaaS. Collaborate with engineers, data scientists, product, and design; own functional areas from development to production; mentor teammates; apply modern dev practices to deliver scalable, user-centric solutions.

Build and deploy production AI agents and MCP integrations for logistics workflows including dispatch, billing, compliance, and fleet operations. Responsibilities include multi-agent orchestration, hybrid RAG/KAG/CAG retrieval architectures, LLM integrations, prompt engineering, structured outputs, and function calling. The role also involves collaborating with operational teams and maintaining Kubernetes-based agent infrastructure using GitOps and observability practices.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, lifecycle, landing pages, growth operations, and product growth. Automate analysis, personalization, campaign execution, content generation, reporting, and anomaly detection. Evaluate AI infrastructure, integrate marketing and data platforms, and partner cross-functionally with Growth, Product, and Engineering to deliver production-grade solutions and improve conversion, retention, and team efficiency.

Lead product vision and strategy for an end-to-end AI-powered software factory. Integrate planning, coding, review, and verification across GitLab Duo Agent Platform and product areas. Collaborate with engineering, design, and GTM teams, identify gaps and dependencies, present recommendations to executives, and drive execution to deliver cohesive developer workflows.

Lead establishment of an Agile & AI performance measurement practice: define frameworks, KPIs, tooling, governance, analytics platform strategy, and executive storytelling to measure delivery flow, quality, value, and GenAI/automation impact. Build and lead the team and partner across functions to turn insights into operating decisions and improvements.

Lead creation of an enterprise Agile & AI performance measurement practice. Define frameworks, KPIs, tooling strategy, governance, and analytics to measure delivery flow, predictability, quality, value, and the impact of GenAI/automation. Build a high-performing team, translate metrics into executive insights, and partner across engineering, finance, and transformation to drive informed investment and operating model decisions.

Conduct research and develop state-of-the-art speech and audio AI technologies. Responsibilities include literature reviews, algorithm implementation, data collection and analysis, deep learning and signal processing, and documenting, presenting, and publishing results while collaborating with researchers under supervision.

Conduct embodied and physical AI research, implement and deploy machine learning algorithms into internal products, collaborate with researchers, and stay current on machine learning developments. Prepare insight and technical reports. The role requires deep learning knowledge and strong Python and PyTorch coding skills; experience with large language models and robotics is preferred.

Develop and maintain software tools and infrastructure supporting machine learning and AI workflows. Build backend services, APIs, automation tools, and integrations; support large-scale experiments, evaluation, and data processing. Debug issues, improve reliability and performance, collaborate with researchers and engineers, gather user feedback, provide technical support, and write clean, tested code.

Develop, test, and maintain GenAI/ML-based application components; implement new features, unit tests, and fixes; collaborate with stakeholders and tech partners; participate in code reviews, incident management, and continuous improvement; mentor team members and ensure solutions meet performance, security, and governance standards.

Lead AI risk assessments and governance for the model lifecycle. Ensure AI solutions meet enterprise policies and regulations, maintain risk registers, monitor controls, partner with Privacy/Security/Legal, drive remediation, and report AI risk posture to leadership while improving governance frameworks to enable responsible AI adoption.

Lead AI risk assessments and governance processes to ensure AI solutions meet enterprise policies and regulatory requirements. Monitor model lifecycle risks, maintain risk registers, coordinate remediation, and partner with Privacy, Security, Legal, and business teams. Develop reporting and improve AI governance frameworks to enable responsible innovation.

Drive adoption of AI governance by partnering with AI/product teams to embed requirements, run risk assessments and approvals, lead enablement and training, produce practical tools and templates, monitor governance adoption and maturity, and streamline governance processes to support responsible AI at scale.