Top AI Jobs in Toronto

Lead enterprise AI architecture strategy and roadmap; design scalable AI, data, and integration architectures; establish reusable frameworks, standards, and governance; drive platform and technology selection; ensure security, compliance, and responsible AI; partner with business leaders and mentor teams to deliver enterprise-scale AI solutions.

Leads revenue operations for a B2B SaaS company by owning forecasting, pipeline rigor, GTM accountability, CRM architecture, data quality, KPI reporting, and AI-enabled automation. Partners with Sales, Marketing, Customer Experience, Product, and Finance to operationalize strategy, improve revenue predictability, and align targets, processes, and systems. Manages and mentors a RevOps Analyst while building a scalable, high-performing function.

Own the company’s platform end to end across full-stack product development, Kafka and ClickHouse data/ML pipelines, cloud infrastructure, Kubernetes, Terraform, CI/CD, observability, security, and incident response. Build React and Node.js features, operate production systems, replace vendor-dependent infrastructure, and establish AI-assisted engineering workflows using tools such as Claude Code and Cursor. This is a hands-on technical leadership role for a senior individual contributor in a lean startup.

Designs, deploys, and maintains production machine learning systems for identity resolution, scoring, deliverability, and personalization. Builds batch and real-time feature pipelines, develops classification models, integrates ML with graph databases, and manages monitoring, drift detection, retraining, and performance reporting. Partners with engineering to deliver low-latency APIs and scalable services while contributing to GPU-accelerated processing, MLOps standards, and large-scale graph computation.

Own the production deployment of neural and generative models for Waabi’s multi-sensor simulator. Responsibilities include optimizing models with compilation, quantization, mixed precision, CUDA, and custom kernels; building numerical parity benchmarks; developing model export, versioning, registry, and build tooling; collaborating with research teams; and managing GPU workloads in cloud environments.

Lead Westland’s Future Ready Workforce program by translating AI and business transformation priorities into capability, learning, adoption, and workforce-readiness initiatives. Responsibilities include managing program plans, governance, budgets, risks, dependencies, and executive reporting; coordinating capability and skills-gap analysis; building an AI Champion Network; developing role-based learning resources; supporting responsible AI adoption; and tracking workforce readiness, capability, adoption, and business impact measures.

Leads technical sales and customer engagements for Azure cloud and AI infrastructure modernization. Designs secure, scalable, resilient, compliant, and sovereign architectures; guides migrations involving Windows, Linux, Oracle, applications, databases, and virtual machines; and develops hybrid networking solutions. Conducts workshops, demonstrations, proofs of concept, pilots, and architecture sessions to resolve technical challenges and influence Azure deployments. Advises customers on containerization, AKS, Agentic AI tooling, enterprise platforms, security, compliance, and resiliency.

Conduct research and develop algorithms for audio, speech, and acoustic AI using signal processing and deep learning. Build acoustic data pipelines, implement Python and C++ prototypes, evaluate models, and collaborate with engineering teams to productize successful research. The role may involve research publication in areas including speech recognition, audio generation, acoustic enhancement, spatial audio, and source separation.

Serve as the Finance-focused functional expert enabling GSI and ecosystem partners on Workday Financials and agentic AI. Develop playbooks, translate roadmap into outcomes, support partner positioning, demos, competitive messaging, and regulatory/risk considerations for finance AI use cases. Collaborate with solution architecture, solution consulting, and partner teams to ensure partner readiness, demo fidelity, and go-to-market success.

Lead Block’s global tax planning and controversy agenda, including international and U.S. tax strategy, transfer pricing, M&A taxation, legal-entity structuring, tax audits, examinations, disputes, and transaction negotiations. Advise senior leadership on tax implications, partner with Legal, Treasury, Corporate Development, and finance teams, monitor legislative developments, and ensure tax positions are reflected in reporting and compliance. Build high-performing teams and represent Block before domestic and foreign tax authorities.

Conduct AI-driven security assessments across software, hardware, and firmware; develop AI-enabled offensive and defensive security tooling; identify and remediate vulnerabilities; test LLMs, agentic workflows, and cloud environments; and guide organization-wide AI security strategy. Partner with engineering and leadership, lead threat-modeling sessions, and communicate security findings and recommendations to senior stakeholders.

Own the product strategy and roadmap for Glean Model Hub. Evaluate LLMs, manage model and inference-provider relationships, support customer experimentation, and oversee usage, cost, and capacity planning. Partner closely with customers and cross-functional teams to prioritize capabilities, enable adoption of new models, improve AI workflows, and build scalable processes. The role requires strong technical product management experience spanning enterprise SaaS, AI/ML operations, APIs, UX, and third-party partnerships.

Improve the quality and reliability of Glean’s AI assistants and agents through evaluation-driven development, model routing, retrieval, prompting, and post-training. Build supervised and preference-optimized models, reinforcement-learning environments, evalsets, LLM judges, and agentic verifiers. Analyze production traces, calibrate evaluation signals against human graders, balance quality with latency and cost, and partner with product, platform, research, and customer teams to ship production ML improvements.

Build data and evaluation systems for Glean’s AI products, including benchmark datasets, scalable eval infrastructure, agent observability, LLM-based verifiers, CI and production evaluation pipelines, and enterprise simulation environments. Analyze traces and failures, measure quality with statistical rigor, and translate findings into product improvements. Partner with research, engineering, product, and go-to-market teams to improve agent performance while balancing quality, latency, and cost.

Leads legal operations initiatives, including metrics, dashboards, reporting, workflow redesign, intake management, and AI-assisted automation. The role improves scalable legal processes, manages team queues, supports contract review workflows, and partners cross-functionally to deliver operational efficiencies. It requires 5+ years of relevant operations, analytical, or technical experience, strong project management and communication skills, and hands-on experience with generative AI and legal operations tools.

Build Retell AI’s legal function and serve as Sales’ primary partner on enterprise transactions. Responsibilities include negotiating MSAs, DPAs, BAAs, and order forms; creating contract templates, negotiation playbooks, and turnaround SLAs; advising on voice AI issues such as consent, TCPA, AI disclosure, and use-case reviews; and managing platform terms, privacy policies, acceptable-use policies, and outside counsel.

Lead the design and delivery of production-grade AI agents and automation workflows. Translate ambiguous business processes into reliable software, integrate agents with systems of record using MCP, and establish architecture, evaluation, monitoring, safety, auditability, human review, and rollback practices. Partner with data and business teams, drive reusable technical patterns, mentor engineers, and document systems for sustainable operation.

Leads Kinaxis’s Advanced Mathematical Modelling function, managing architects, algorithm developers, and operations research specialists. Provides technical and strategic leadership across optimization, machine learning, simulation, decision science, and algorithm development. Partners with Product Management, Engineering, and customer teams to solve supply chain challenges, guides research and prototyping, shapes platform architecture, and promotes scalable production solutions. Oversees talent acquisition, coaching, workforce planning, performance management, and career development while establishing the function’s vision and operating model.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.

Leads enterprise technology strategy, delivery, and transformation across Finance, FinTech, Legal, and Risk. Oversees product platforms, Agile and DevOps adoption, AI and data initiatives, digital transformation, change management, vendor partnerships, governance, and technology operations. Partners with executive stakeholders to align global priorities, integrate platforms, manage dependencies, and deliver scalable, secure, value-driven outcomes across a federated enterprise technology organization.

Design, build, and operate production AI features and infrastructure for Snowflake, including model serving, inference pipelines, embeddings, RAG, and intelligent applications. Develop scalable, tested software; collaborate with researchers and product managers; contribute to architecture and technical direction; and own systems through deployment, monitoring, and operational support.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for engineering judgment, response quality, reasoning, usefulness, and trustworthiness. Provide clear written and recorded feedback, distinguish quality levels, and help define standards for excellent AI-assisted development workflows. This is a flexible, remote contract role focused on evaluation rather than production coding.

Design, build, and maintain production-grade AI-powered go-to-market automations (prospecting, lead scoring, campaign orchestration, attribution). Integrate ML/LLM tools into marketing and sales workflows, track business-critical metrics, ensure reliability and scalability, and collaborate with GTM strategists and non-technical stakeholders to drive measurable pipeline and revenue outcomes.

Leads an Investment Insights team creating analytics that improve investment manager effectiveness. Responsibilities include designing and developing investment analytics, researching methodologies, working with portfolio managers, productionizing solutions, applying AI, resolving complex analytical issues, and managing analysts across Canada and India. The role also shapes strategic priorities, develops team capabilities, mitigates process risks, and communicates technical concepts to varied stakeholders.

Develop investment analytics that improve investment manager effectiveness and decision-making. Research analytic methodologies, build proofs of concept, work with investment teams and leadership, and productionalize solutions with platform teams. Analyze financial datasets across performance, risk, attribution, liquidity, ESG, fees, and trading. Apply AI to financial modeling, anomaly detection, and natural-language data queries while resolving complex issues and communicating technical concepts clearly.