Top AI Jobs in Toronto

Develop and prototype AI applications for market and counterparty credit risk policy development and management. Collaborate with stakeholders to translate business problems into analytical solutions, conduct exploratory data analyses, identify actionable insights, and automate workflows. The role involves experimenting with LLMs, generative AI, prompt engineering, MCP integrations, machine learning, and deep analytics while communicating technical findings to non-technical stakeholders.

Design, develop, and deliver enterprise full-stack applications using Java, Spring Boot, and Angular. Provide technical leadership through architecture, code reviews, mentoring, and hands-on problem solving. Embed AI coding tools and establish team standards for prompt engineering and quality. Apply relational and NoSQL data design with Oracle and MongoDB. Support cloud-native engineering through Docker, OpenShift, Kubernetes, infrastructure as code, CI/CD, and modern software delivery practices.

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 design and deliver enterprise applications using Pega and integrated AI capabilities. Set technical standards, perform systems analysis, drive end-to-end delivery, mentor mid-level engineers, partner with stakeholders, and ensure compliant, scalable architecture and implementations.

Lead a team to provide production support for mission-critical Pega and AI platforms. Own incident management using observability tools, manage vendors/offshore partners, drive automation with Python/Java, improve reliability, capacity and onboarding standards, mentor engineers, and align support priorities with business and engineering stakeholders.

Design, build, and maintain automated test suites, evaluation scenarios, and CI/CD quality gates for AI-powered applications. Test APIs, validate ML model outputs, create measurable evaluation rubrics, mentor teams, and contribute to shared QA frameworks and codebases.

Lead hands-on design, prototype, and deploy end-to-end AI/ML and Generative AI solutions (LLMs, RAG, agents). Build MLOps/LLMOps pipelines, integrate vector DBs, ensure observability and governance, collaborate cross-functionally, and mentor engineers to move projects from prototype to production.

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.

Lead development of ML systems for retailer acquisition and engagement: bidding optimization, targeting, AEO/LLM-driven content, causal inference experiments, and scalable marketplace solutions working cross-functionally.

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.

Own and operate Baseten's internal AI stack, build and deploy custom AI agents and LLM-powered tools, evaluate third-party AI developer tools, instrument usage and measure impact on engineering productivity, and support engineering teams in adopting AI workflows.

Lead design and delivery of production-grade iOS agent solutions. Implement agent workflows, integrations, CI/CD, testing, observability, and secure features. Provide technical leadership, mentor engineers, drive code quality, and ensure implementations meet business and regulatory requirements.

Lead design and delivery of production-grade Android agent solutions: implement agent workflows, integrations, and human-in-the-loop controls; set squad engineering standards; mentor engineers; drive testing, CI/CD, observability, and reliable deployments; collaborate with architects, strategists, and managers to align implementations with business and regulatory requirements.

The AI Solutions Engineer will build and deploy AI tools, integrate workflows across brands, and enhance operations and marketing processes through automations, focusing on performance marketing and cross-functional collaboration.

Lead end-to-end design for AI-powered developer tools at GitLab. Create flows, wireframes, prototypes, and high-fidelity interfaces for AI agents, chat, and code-assist features. Define success metrics, iterate with PMs and engineers, establish AI-specific design patterns in the design system, mentor other designers, and contribute to product strategy and research-informed decisions.

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.

Build and maintain cloud-native AI platform services, implement IaC automation, develop developer tools and APIs, support deployment, monitoring, scaling of ML models, collaborate with engineers and data scientists, contribute to CI/CD and DevOps practices while applying security, observability, and governance best practices.

As an AI Operations Developer, you will manage AI-driven projects, develop workflows, and support marketing automation to enhance team efficiency and effectiveness.

Design, build, and deploy AI and automation solutions (LLMs, Copilot, Power Platform) that integrate with enterprise systems. Rapidly develop MVPs, implement prompt engineering and RAG strategies, create APIs and workflows, perform testing and UAT, support deployments, and maintain architectural and security documentation while collaborating with stakeholders and security teams.

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.

Lead end-to-end design and delivery of high-performance, interactive FinTech analytics and reporting platforms. Build Angular/React frontends and Java/Spring Boot low-latency microservices, optimize ETL and Starburst (Trino) queries, create MCP servers to connect LLMs/AI agents to enterprise data, and implement Aspose-based document generation. Mentor engineers and validate AI-generated code with CIB domain expertise.

Lead a shared AI operations function delivering agent configuration, evaluation, and knowledge support to client-facing squads. Manage capacity, demand forecasting, workload planning, service standards, and operational metrics. Drive adoption of shared AI assets, resolve cross-team dependencies, and continuously improve service quality. Coach and develop team members, establish intake/prioritization/escalation processes, and ensure reliable, scalable delivery of AI-enabled solutions across multiple client engagements.

Prepare, structure, govern, and maintain knowledge assets for AI agents. Perform content cleanup, metadata tagging, taxonomy management, research, documentation, and governance to ensure compliant, auditable, and production-ready knowledge for regulated banking engagements.

Lead the AI Platform team, define AI strategy, deliver AI capabilities, mentor engineers, and foster a culture of innovation and collaboration.

Embed with client operators, learn and rebuild critical business processes, and deliver production GenAI/LLM systems on AWS. Own design, shipping, evaluation, and operational handoff while working cross-functionally with senior stakeholders to move business metrics.