Top AI Jobs in Toronto

Serves as a principal technical bridge between enterprise customers and engineering, architecting developer experiences, APIs, Kubernetes platforms, GitOps workflows, and LLM infrastructure. Builds production Go, Python, and TypeScript tooling, operators, controllers, SDKs, and AI integrations. Designs scalable inference, RAG, agentic, distributed compute, and data platforms; mentors engineers and converts field feedback into product-roadmap requirements. Requires extensive software engineering, DevEx, API, Kubernetes, and generative AI experience.

Lead corporate affairs and public policy communications for an AI video company. Develop positions on responsible AI, creative rights, safety, privacy, and content moderation; manage sensitive issues and external responses; prepare executives for media, policymaker, and stakeholder engagement; and coordinate messaging across Legal, Product, Trust & Safety, Communications, and enterprise teams.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Design, prototype, and ship production AI/ML features that analyze observability data to detect, triage, and resolve incidents. Rapidly iterate on LLM- and agent-powered workflows, collaborate cross-functionally, integrate AI with internal tools and alerting systems, and ensure solutions are scalable, maintainable, and aligned with user workflows.

Leads architecture and technical strategy for complex cloud, data, and AI/ML systems. Designs scalable multi-cloud platforms, data pipelines, MLOps and LLM applications; establishes infrastructure, DevOps, security, governance, and observability standards. Partners with clients and leadership, leads modernization initiatives, resolves difficult integration challenges, mentors senior engineers, and evaluates emerging technologies.

Build and operate production AI infrastructure powering intelligent agents at scale. Develop high-performance Rust services for model inference, orchestration, and execution; design scalable distributed systems; establish MLOps, observability, guardrails, and failure recovery; and optimize latency, throughput, reliability, and cost. Partner with agent systems and infrastructure teams to turn prototypes into hardened services serving millions of users.

Lead the strategy, vision, and multi-quarter roadmap for Elastic’s vector database, embedding and reranking capabilities, and core search use cases. Conduct market and model research, define product metrics, evaluate competition, prioritize features, and coordinate engineering, cloud, research, and business teams. Own technical trade-offs involving vector indexing, retrieval quality, latency, recall, ingestion throughput, and cost while guiding developer-facing APIs and infrastructure products across serverless and self-managed deployments.

Own the Commercial SMB customer segment, driving onboarding, adoption, retention, and expansion. Build a proactive, AI-assisted customer success model; lead SMB onboarding and AI agent rollout; design scalable workflows, processes, and playbooks; coordinate with Sales, Marketing, RevOps, Support, Product, and AI teams; and represent SMB customer needs across the organization.

Leads Colliers’ enterprise data engineering strategy and a team of data engineering managers and engineers. Oversees scalable multi-cloud data platforms, microservices, data mesh and fabric capabilities, AI/ML pipelines, GenAI workflows, analytics, and business intelligence solutions. Establishes DataOps, governance, security, CI/CD, and reliability standards while managing platform operations, incident response, service levels, technology roadmaps, and cross-functional partnerships.

Build and deploy production GenAI and LLM systems for enterprise clients in financial services and healthcare. Embed with customers to understand operational needs, design AI solutions, own implementation from strategy through deployment, develop evaluation suites, and improve reusable industry blueprints. Advise senior stakeholders, contribute to pre-sales, and help shape AI delivery tooling and frameworks using Claude, AWS, Python, and TypeScript.

Lead product marketing strategy and execution for the US Home Care business. Own the tri-annual release process, develop positioning and competitive differentiation, build buyer personas and journeys, and create internal enablement materials. Partner with Product, Sales, Customer Success, Education, and Marketing to drive feature adoption, retention, launch effectiveness, and commercial impact. Conduct market research, support thought leadership, refine pricing and packaging, and responsibly apply AI tools.

Oversee AI risk across assigned portfolios, providing second-line challenge, regulatory and audit coordination, assurance oversight, and emerging regulatory intelligence. Lead regulatory examinations, audit responses, executive reporting, cross-functional workplans, governance routines, and risk escalation. Monitor global AI regulations and translate developments, audit findings, and industry practices into enhancements to AI governance frameworks, controls, and risk management processes. Influence business, technology, compliance, legal, and audit stakeholders while representing AI Risk with regulators and senior leadership.

Develops machine learning, deep learning, and predictive models using large structured and unstructured datasets. Mines data, identifies trends, evaluates AI/ML solutions, and creates scalable analytics processes to support business decisions, customer experience, and productivity. Collaborates with product teams, analytics professionals, and senior stakeholders to provide recommendations, shape roadmaps, and address complex business problems while managing risk within banking policies and regulations.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Build backend services, data pipelines, APIs, distributed systems, developer tools, and shared platform capabilities for AI products. Develop ML experiment tracking, artifact management, and automated training and evaluation pipelines. Improve reliability, latency, and throughput while collaborating with Product and ML teams. Own scoped projects involving implementation, testing, maintenance, and clean API design for enterprise and government customers.

Develop and execute enterprise sales and go-to-market strategy for emerging AI agent products. Build executive relationships, identify use cases, manage sales from discovery through closing, and partner with product and engineering on positioning, packaging, pricing, pilots, and product requirements. Establish repeatable processes for converting experimentation into production deployments while providing market feedback and shaping a new product category.

Develop scalable Java applications and microservices using Spring, while applying AI-assisted development and prompt engineering throughout the SDLC. Build and maintain CI/CD pipelines, cloud deployments, containerized services, and observability solutions. Collaborate with product managers, designers, and engineers on architecture, testing, documentation, and complex problem-solving. The role also involves reviewing AI-generated code, working with front-end technologies and databases, and applying agile, TDD, or BDD practices.

Designs and delivers end-to-end AI solutions for business and customer problems, including agents, tools, retrieval, integrations, workflows, and user interfaces. The role gathers requirements, prototypes solutions, defines architecture and testing, uses AI coding tools, and moves products from validation to production. It also establishes success metrics, evaluates AI quality and business impact, and collaborates with Product, UX, SRE, QA, software development, analysts, customers, and executives.

Lead development of Maestro, an AI mortgage underwriting and lending engine. Design agentic AI workflows combining rules, LLMs, retrieval, and structured extraction; build model evaluation and monitoring pipelines; guide architecture and code quality; ship production AI features; mentor peers; and lead cross-functional projects from planning through delivery.

Lead end-to-end development of AI underwriting and lending features, including agentic workflows combining rules, LLMs, retrieval, and structured extraction. Build model evaluation and monitoring pipelines, shape AI platform architecture, review code, mentor peers, and guide technical delivery. The role requires strong Python, backend/API, software engineering, data science, and production LLM experience, with opportunities to work in regulated financial services.

Build and lead Tailscale’s internal AI enablement capabilities, including developer tools, workflows, coding-agent practices, evaluation processes, and safety guardrails. Partner with engineering and other teams to identify high-impact AI opportunities, drive adoption, measure productivity and quality outcomes, and communicate recommendations to leadership. The role requires hands-on software engineering, internal tooling, AI coding-tool expertise, strong communication, and comfort working independently amid ambiguity.

Leads BMO InvestorLine’s AI strategy, portfolio, and multi-year roadmap. Identifies and prioritizes opportunities, develops business cases, defines AI product visions, and guides products from discovery through launch, adoption, optimization, and value realization. Partners across technology, data, design, operations, risk, legal, compliance, and vendors while ensuring responsible AI, privacy, security, and regulatory compliance. Advises executives and drives measurable business and client value in a regulated financial-services environment.

Lead design and delivery of scalable, governed data ingestion and integration pipelines for structured, semi-structured, and multimodal data to enable AI, ML, and Generative AI. Partner with product, data science, architecture, risk, and operations to define requirements, ensure data quality, lineage, security, and observability, and evaluate new technologies. Mentor engineers, drive cross-functional initiatives, improve platform resilience, and measure ingestion and AI data effectiveness.

Leads Okta’s global paid media and performance marketing organization, defining strategy across search, social, programmatic, ABM, and emerging channels. Owns multimillion-dollar media investment, pipeline contribution, attribution, ROI, agency governance, and executive alignment. Builds global teams and operating models while advancing AI-driven audience modeling, bidding, creative, measurement, and developer acquisition. Partners with marketing, sales, finance, product, analytics, and data engineering teams to connect media investment with revenue and customer acquisition efficiency.

Leads complex application solution design and development across technologies and platforms. Translates business requirements into scalable system designs, infrastructure, interfaces, and integrations; contributes to technology roadmaps; drives design reviews; makes technology selections; and mentors junior designers and developers. Requires extensive experience with databases, cloud platforms, ETL, source control, issue management, and application implementation and support.