Top AI Jobs in Toronto

Manage and optimize infrastructure supporting AI and machine learning systems, including cloud environments, GPU/CPU clusters, model deployment, data pipelines, security, monitoring, troubleshooting, automation, and CI/CD. The role develops scripts, maintains documentation, supports AI teams, and improves system reliability and scalability. Candidates need a relevant bachelor's degree, at least two years of systems administration or infrastructure experience, cloud and containerization expertise, scripting skills, and familiarity with machine learning frameworks and deployment pipelines.

Supports responsible AI governance by developing frameworks, conducting risk and impact assessments, evaluating fairness and transparency, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight processes. Collaborates with technical, legal, compliance, risk, and business teams to promote ethical, accountable, and trustworthy AI deployment.

Designs, tests, evaluates, and optimizes prompts for large language models and AI applications. Develops reusable prompt templates, conducts experimentation and A/B evaluations, monitors AI performance, and establishes quality standards. Collaborates with AI engineers, product managers, trainers, and business stakeholders to translate requirements into scalable prompting strategies. Researches generative AI advancements, supports model evaluation and tuning, documents best practices, and recommends improvements to response quality, consistency, accuracy, and business relevance.

Leads business analysis for AI and automation initiatives by identifying adoption opportunities, developing business cases, gathering requirements, facilitating stakeholder workshops, supporting solution design and delivery, measuring benefits, and contributing to AI governance and continuous improvement.

Evaluates and validates AI chatbot and conversational AI performance through test plans, regression testing, user acceptance testing, and conversation analysis. Documents defects, verifies business requirements, identifies conversational gaps, reports findings, and recommends improvements. Collaborates with AI trainers, prompt engineers, and development teams to optimize chatbot quality, compliance, reliability, and user experience. Supports evaluation frameworks, testing standards, and continuous improvement initiatives.

Develop, deploy, and optimize scalable machine learning solutions, including predictive, deep learning, and intelligent automation models. Build data pipelines, prepare large datasets, monitor production models, manage retraining, and ensure quality, security, compliance, and responsible AI practices. Collaborate with data scientists, engineers, product teams, and stakeholders while researching emerging AI technologies and improving AI architectures.

Oversee and optimize infrastructure supporting AI and machine learning platforms, including servers, cloud environments, compute resources, GPU/CPU allocation, security controls, monitoring, and incident response. Design scalable AI environments, automate operational processes, troubleshoot system issues, and support model deployment and training workloads. Mentor junior administrators, evaluate emerging technologies, and recommend improvements to AI platform architecture, reliability, security, and scalability.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

Build and ship end-to-end ML/AI systems (traditional ML, NLP, LLMs/AI agents) on transaction, merchant, and location data. Prototype, productionize, and monitor models, collaborate with engineering and product, and (for senior hires) shape roadmap and mentor teammates.

Leads architecture for Fortive’s HR technology ecosystem, including Oracle HCM, Boomi, ServiceNow HRSD, Eightfold, LMS platforms, and enterprise integrations. Defines API-first integration strategies, data governance, security standards, and modernization roadmaps. Designs and deploys AI, automation, NLP, and chatbot capabilities for HR workflows. Advises HR and IT stakeholders, guides vendors and technical teams, and mentors integration developers while ensuring compliance, data quality, and scalable platform architecture.

Own the technical sales motion for NVIDIA-focused enterprise AI infrastructure deals. Lead discovery, architecture, proof-of-concept design and execution, technical closes, partner and field SE enablement, troubleshooting, escalation, customer workshops, and product feedback. The role requires expertise in NVIDIA systems, storage, data platforms, Linux, parallel file environments, GPU clusters, and AI pipelines, with Bay Area availability for on-site partner or customer sessions.

Build and productionize AI agents for product analytics, integrating LLMs, tools, retrieval pipelines, and data systems. Develop data pipelines, evaluation frameworks, guardrails, telemetry, and monitoring. Diagnose agent quality issues, improve prompts and context, and contribute to proactive product intelligence systems. Collaborate with product and engineering teams to translate business needs into reliable agent designs while documenting work and following shared development standards.

Lead end-to-end product design for AI-powered experiences across web and mobile. Simplify complex workflows for frontline users, translate ambiguous AI concepts into testable solutions, contribute to scalable design systems, present design rationale, incorporate feedback, and partner closely with product and engineering teams. The role requires strong visual and interaction design, systems thinking, storytelling, and experience designing AI features such as natural language interfaces, agents, and predictive insights for B2B SaaS or enterprise products.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Conduct frontier research on data-efficient agentic AI, including model architectures, meta-learning, test-time adaptation, and reinforcement learning. Train deep learning models at scale, deploy agentic pipelines, design experiments and evaluations, analyze results, communicate findings, participate in brainstorming, support academic papers and technical reports, and stay current with machine learning publications.

Lead the Agentic AI Lab’s full-cycle research agenda, including model and training design, data, evaluation, benchmarking, deployment, and publication. Train specialized enterprise models, define rigorous evaluation gates, move research from public benchmarks to customer shadow deployments, and build a small multidisciplinary team. The role partners closely with founders and platform engineers while establishing research direction, documenting technical decisions, and mentoring engineers into researchers.

Design, train, evaluate, and deploy production computer vision systems for industrial applications. Build detection, segmentation, classification, OCR, tracking, and visual-language pipelines; optimize real-time inference for edge and cloud environments; develop data and evaluation pipelines; fine-tune multimodal foundation models; and improve performance through quantization, pruning, and hardware acceleration. Collaborate across AI, engineering, product, and research teams on architecture, technical design, and production-grade AI systems.

Design and operate production backend services and AI infrastructure, including model serving, inference, data pipelines, vector stores, RAG systems, APIs, and microservices. Architect scalable real-time and batch systems, integrate and deploy LLM, computer vision, and machine learning models, and ensure reliability, observability, testing, and CI/CD. Collaborate with ML, product, and customer-facing teams while documenting systems and mentoring engineers.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Build and prototype enterprise AI solutions, including agents, MCP integrations, Copilot extensions, and automations. Partner with business and technical stakeholders to identify valuable use cases, evaluate emerging AI technologies, and move successful proofs of concept toward production. Maintain reusable AI assets, document solutions for engineering handoff, advise leadership on AI strategy, and support responsible, secure, scalable adoption across the organization.

Leads PointClickCare’s HR technology roadmap, Workday migration, AI-agent deployment, workflow redesign, data governance, and responsible AI controls. Oversees HR technology teams, vendors, budgets, configuration, testing, cutover, hypercare, adoption, and change management. Partners cross-functionally to improve HR service delivery, establish trusted data and reporting, and redesign processes for safe automation and effective AI-human collaboration.

Owns the build, hardening, patching, configuration, and lifecycle management of enterprise operating-system and platform infrastructure. Leads major incidents, root-cause analysis, change control, service improvement, and technical escalations. Identifies and scales AI and automation for incident triage, runbook execution, predictive monitoring, and reduced MTTR. Provides solution designs, implementation plans, standards, documentation, stakeholder influence, team mentoring, vendor coordination, and AI governance.

Designs, develops, deploys, and supports production-grade Generative AI and Agentic AI solutions. Builds RAG systems, multi-agent architectures, AI copilots, reusable AI frameworks, orchestration workflows, and enterprise integrations. Collaborates with product, business, data, and engineering teams; ensures scalability, reliability, observability, security, privacy, governance, and regulatory compliance. Evaluates and improves AI applications, contributes to platform strategy, communicates technical decisions, and mentors junior technical staff.

Leads consultative AI sales to enterprise accounts, from discovery and pursuit management through proposals, negotiation, contract signature, delivery partnership, renewals, and account expansion. Develops the client narrative, value case, pricing structure, and commercial strategy while collaborating with AI and consulting leaders on solution design. Targets executive buyers across retail, financial services, manufacturing, automotive, and healthcare, with performance measured by booked revenue, pipeline, win rate, engagement size, retention, and account growth.