Top AI Jobs in Toronto

As an AI/ML Engineer, you'll develop and optimize machine learning models for mobile applications, collaborate with teams, and integrate AI-driven features to enhance user experience.

Embed with customers to learn their workflows, redesign and build production GenAI/LLM systems on AWS, ship and operate code, evaluate non-deterministic models, and own business outcomes.

Lead Internal Audit Analytics & AI strategy; integrate analytics and AI across the audit lifecycle; develop scalable analytics, automation and responsible AI solutions; manage and mentor an analytics team; provide technical direction for Power BI, Alteryx, SQL, Python and LLM tools; partner with audit and business stakeholders and present insights to senior management.

Participants will learn to design intelligent AI workflows and automation tools using modern AI technologies and publish their own projects within eight weeks.

Manage AI compute clusters, monitor systems health, optimize resources, and troubleshoot technical issues, ensuring high performance for ML applications.

Manage and grow a small ML/GenAI engineering team while remaining hands-on: design and implement GenAI systems (agents, RAG, tool use), build NLP solutions (NER, classification, retrieval, summarization), partner with PMs and engineers to scope and ship features, and coach engineers on technical and career growth.

As a Lead Software Engineer, you will design AI-driven backend systems for accounting, integrating LLMs, ensuring performance and security, and collaborating with teams on AI solutions.

The Risc-V Architect will analyze AI workloads, design custom RISC-V cores, and lead the architecture efforts at Tenstorrent, improving CPU performance.

Lead design and production of next-generation search and personalization systems using LLMs, dense vector retrieval, GNNs, multi-stage ranking, and reinforcement learning. Drive GPU-based deployment (e.g., Triton), ensure <100ms latency, mentor applied scientists and MLEs, and partner cross-functionally to productionize scalable, reliable search and recommendation experiences.

Build and maintain backend features for Agent Observability across the GitLab monolith and AI gateway. Design and implement GraphQL/REST APIs, improve automated tests (RSpec), collaborate cross-functionally, troubleshoot production issues on Tier 2 on-call, and help shape responsible AI integration and platform architecture.

The Program Manager, AI Activation oversees AI initiatives, ensuring effective governance, financial tracking, and delivery excellence while managing Project Managers.

Design, develop, and deploy Java and Spring Boot backend applications using MongoDB. Build and integrate AI agents with Google ADK and apply prompt engineering techniques to large language model solutions. Support feasibility studies, estimation, planning, testing, implementation, security, compliance, and business-user operations. Serve as an application development subject matter expert while managing projects independently and advising senior stakeholders.

Own the partner-facing product backlog for an AI-first payment gateway migration. Translate commercial and technical partner needs into requirements, user stories, API specifications, acceptance criteria, and delivery plans. Collaborate closely with Engineering, Product, Risk, Commercial, and Partner Success to prioritize, validate, launch, and iterate payment features. Lead pilots, UAT, migration cutovers, stakeholder communication, and partner discovery. Apply AI tools throughout requirements, prototyping, testing, documentation, and software delivery.

Own the strategy, qualification frameworks, routing logic, handoffs, governance, and performance optimization for AI-assisted xDR capabilities. Lead cross-functional alignment across Marketing, Sales, xDR, Revenue Operations, and Sales Operations. Translate business requirements into workflow specifications, guide implementation across GTM platforms, measure funnel outcomes, and optimize AI-to-human engagement, routing, and pipeline progression.

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.

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.

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.

Manage enterprise SaaS implementations from pre-sales discovery through launch and post-launch adoption. Responsibilities include data migrations, project plans, custom integrations, AI Agent configuration, QA, prompt optimization, and automation strategy. The role coordinates cross-functional teams and merchant stakeholders, drives timelines and accountability, analyzes e-commerce and support data, and builds trusted customer relationships to achieve successful launches and measurable adoption outcomes.

Conduct advanced computer vision research in 2D/3D scene understanding, tracking, reconstruction, sensor fusion, and real-time perception. Integrate vision and language models with LLMs, optimize pipelines for embedded hardware, and develop evaluation frameworks for algorithmic and human-centered outcomes. Collaborate with engineering, hardware, and design teams to convert research into product-ready solutions and interactive prototypes.

Lead end-to-end delivery of AI and automation initiatives, manage the automation workstream within an ERP transformation, perform business analysis and process design, triage intake and prioritize backlog, run discovery workshops, collaborate with RPA/automation developers, and lead system, integration, and UAT testing while managing stakeholders and project communications.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Act as a technical advisor to academic AI labs working on foundational models. Identify research workloads suited to NVIDIA platforms, engage researchers to remove infrastructure blockers, track frontier research, partner with internal teams to drive adoption, translate academic feedback into product and program improvements, and support NVIDIA participation at major research venues through content, workshops, and lab programs.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.