Top AI Jobs in Toronto

Design, build, and deliver scalable Salesforce solutions across Sales Cloud, Data Cloud/Data360 and Agentforce. Lead data integration, identity resolution, data modelling, AI-assisted workflows, Flow/Apex development, LWC/Visualforce components, and platform administration. Own end-to-end delivery, governance, data quality, and release management while partnering with sales and data teams to implement automation and AI-enabled sales workflows.

Lead enterprise-scale transformation to design and implement an AI-enabled global customer support operating model. Define multi-year AI strategy, organizational design, governance, and success metrics. Influence executives and cross-functional partners, drive change management and adoption, translate strategy into measurable roadmaps, and represent transformation outcomes to senior leadership and external stakeholders.

The Director, AI-Enabled DevOps Transformation will drive sales and growth in strategic accounts, manage client relationships, develop account strategies, and lead complex engagements in SDLC modernization and AI-enabled delivery.

Designs enemies and NPC behaviors for a high-mobility multiplayer action game. Creates design specifications and technical documentation, collaborates with designers and engineers on scripting and behavior trees, analyzes player data and feedback to improve AI, coordinates dependencies with producers, and pitches new AI concepts to leadership.

Designs, develops, tests, and supports Java-based cloud-native applications on Google Cloud Platform. Builds REST APIs, integrates AI services, applies secure coding practices, contributes to CI/CD and deployment automation, and implements observability through logging, metrics, dashboards, and alerts. Troubleshoots production issues, performs root-cause analysis, participates in architecture discussions and code reviews, collaborates across engineering and business teams, and mentors junior developers.

Designs, develops, and operates Java-based cloud-native applications on GCP. Builds RESTful APIs, AI-enabled capabilities, secure services, CI/CD pipelines, infrastructure-as-code, and observability practices. Troubleshoots production issues, performs root-cause analysis, supports compliance and secure engineering standards, and collaborates with DevOps, QA, product management, and security teams. Participates in architecture reviews, code reviews, technical documentation, and shared engineering standards.

Build and deploy AI/LLM-powered features and RAG pipelines for internet-scale threat intelligence. Prototype, fine-tune, evaluate, and optimize models; integrate AI into platform services; implement CI/CD, regression testing, and security guardrails; collaborate across product, frontend, and data teams to deliver real-time, low-latency AI-driven analytics and automation.

The AI Infrastructure Engineer designs, develops, and optimizes core capabilities for the AI inference platform, focusing on reliability, scalability, and operability of infrastructure and services.

Drive Azure adoption across telecommunications enterprise accounts in Central Canada. Build business cases, lead virtual sales and partner teams, influence CXO-level stakeholders, meet Azure consumption targets across Infrastructure, Data, and AI, and remove deployment roadblocks to deliver cloud transformation and customer satisfaction.

Own the product vision, roadmap, and delivery for AI-enabled platforms across Aprio. Translate practice requirements into user stories, lead development lifecycles, coordinate onshore/offshore teams, validate and integrate LLMs and AI tooling, and engage stakeholders while ensuring responsible AI, data privacy, and client confidentiality.

Build and own agentic systems on ClickStack to investigate incidents: design reusable skills, implement MCP servers and SDKs, integrate telemetry, optimize latency/cost, run evals, and collaborate with OSS contributors and customers to make agents production-ready and observable.

Design and build conversational AI agents for customer service, ownership of agent systems from architecture to production, and implement safety and evaluation frameworks.

Lead AI risk assessments, reviews, and monitoring to support AI governance at a financial institution. Evaluate internal and third-party AI solutions, maintain an enterprise AI inventory, analyze controls and risk metrics, build dashboards and reports, and provide quantitative and advisory support across stakeholders to enable responsible AI adoption and regulatory readiness.

Lead strategy and execution for Auth0s AI identity products, owning roadmap, prototyping 0-to-1 features, translating standards into technical requirements, and collaborating with engineering, design, security, and GTM teams to drive adoption among developers and customers.

Build and integrate LLM-powered applications and agents (RAG, vector DBs, agent orchestration) into the PureFacts SaaS platform, design evaluation and safety frameworks, implement APIs and scalable services, and partner with product and engineering to deliver AI copilots and automation that improve revenue analytics and operational efficiency.

Lead design, development, and deployment of ML/AI solutions across the PureFacts platform. Own ML product vision and MLOps for multiple client models, build scalable data pipelines and cloud-based deployments, embed AI-driven automation and LLM features, ensure governance and explainability, and mentor a team of ML engineers to deliver measurable business impact.

Lead frontend design and delivery of AI product experiences using React and TypeScript, mentor engineers, set frontend standards, use AI-assisted tooling, and contribute to backend services in Go and product roadmaps.

Build a vendor-agnostic agent platform and runtime primitives, implement evaluation, reliability, safety, and governance tooling, and develop data grounding, retrieval, and memory systems to support agentic workflows and model serving in production.

Build next generation internal AI coding tools to enhance Stripe's engineering productivity. Collaborate across teams and represent customer experiences effectively.

Design and build scalable AI-powered full-stack applications: develop data pipelines (Dagster, dbt), backend services (Python, FastAPI), responsive frontends (React, TypeScript), integrate LLMs and RAG pipelines, implement MCP for agent workflows, ensure data quality and CI/CD, and collaborate with engineering and business stakeholders.

Lead and deliver large, multi‑workstream business and technology transformation programs. Create roadmaps, manage dependencies and risks, engage executives, enforce governance, track value realization, and coach cross‑functional teams to ensure high‑quality, measurable outcomes.

The Python Developer will create Gen AI solutions, collaborate in Agile teams, write maintainable code, manage project delivery, and ensure compliance with standards.

The role involves building AI inference systems, optimizing GPU performance, and developing benchmarking methodologies for large-scale deployments.

Lead the end-to-end customer-facing conversational agent across chat and voice. Own roadmap, conversation design, partner integrations, and interface UX. Drive daily design in Figma, build prototypes with AI coding tools, define quality metrics (resolution, containment, escalation), and iterate from production data while coordinating GTM and partner teams.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.