Top AI Jobs in Toronto

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 architecture and implementation of an Identity Security Fabric for AI agents. Build scalable backend services, integrate complex APIs, enforce auth and least-privilege for agent access, drive AI-native engineering practices, improve CI/CD/observability, and mentor engineers while partnering with product and design to deliver enterprise-grade identity governance and protection features.

Lead the product strategy and roadmap for a cloud-native, AI-driven security operations platform that unifies video, access control, and communications. Own full product lifecycle: customer research, requirements, prioritization, and cross-functional delivery with engineering, AI, and UX teams to ship agentic AI features that automate incident response and simplify B2B SOC workflows.

Lead design and delivery of LLM-powered agent and retrieval systems: build backend retrieval pipelines, orchestration and evaluation frameworks, optimize ranking and latency, integrate vector DBs and cloud infrastructure, collaborate with product and data teams, and drive production readiness for search and agentic workflows.

Lead user-centered AI-first product design for client projects: run discovery, create flows, wireframes, prototypes and assets, mentor teammates, drive cross-disciplinary collaboration, and present to stakeholders to align product strategy and delivery.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Research and optimize AI and machine-learning systems across desktop and mobile platforms. Evaluate model performance, develop and deploy on-device applications and agents, and improve browser ML products. Apply systems and network expertise, collaborate with privacy and security researchers, and investigate AI risks. The role involves open-source development, performance benchmarking, and use of modern ML frameworks to deliver privacy-focused solutions.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Industry Expert who reviews and defends advanced AI, ML, distributed systems, and forward-deployed engineering projects. Responsibilities include challenging architecture decisions, chairing mock review boards and executive presentations, hosting one or two technical sessions monthly, and serving as the escalation point for instructors. The role requires production-scale engineering judgment, strong technical communication, and expertise in agentic systems, LLM evaluation, infrastructure, observability, model serving, RAG, or enterprise deployments.

Design, build, deploy, and optimize production-grade AI systems, including LLM applications, autonomous agents, multi-agent workflows, machine-learning models, data and inference pipelines, and evaluation frameworks. Develop secure, scalable AI services using retrieval-augmented generation, tool calling, orchestration, and model fine-tuning. Monitor reliability, performance, safety, cost, and business outcomes while applying governance, observability, and responsible AI practices.

Productionize, optimize, and scale LLM and ML inference across multi-cloud and Kubernetes. Deploy GPU-based workloads, implement routing/failover for model services, reduce latency via quantization/pruning/distillation, profile and tune hardware utilization, and build observability, monitoring, and cloud-native guardrails for enterprise AI services.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Lead product discovery and delivery for client engagements, partnering with stakeholders, designers, and engineers to define opportunities, prioritize backlogs, manage roadmaps, and deliver measurable outcomes. Serve as a trusted advisor to senior clients while guiding AI-enabled product development. Apply AI tools for productivity and demonstrate practical understanding of LLM systems, RAG, data grounding, hallucinations, latency, cost, and evaluation.

Architect and implement robust bimanual manipulation algorithms and learning-based robot policies (diffusion, imitation, RL). Build end-to-end autonomy connecting perception to low-level control, write and maintain production Python/C++ code, and scale performance across a fleet in industrial environments.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

As an AI Product Engineer at Puzzle Cats, you will develop and enhance AI-driven mobile game experiences, collaborating across teams to rapidly prototype, ship, and iterate on features that can quickly reach millions of players.

Design, build, and operate secure, scalable AI platform capabilities (IaC, Kubernetes, CI/CD, observability, IAM) to enable enterprise AI delivery. Create reusable services (model/API gateways, inference routing, vector/retrieval), enforce governance and SLOs, optimize cost/performance, and mentor engineers while partnering with security and architecture stakeholders.

Design and build secure, automated developer platforms and CI/CD pipelines for regulated enterprises. Implement software supply chain security, policy-as-code, governance automation, deployment strategies, observability, and reusable platform templates. Lead architecture, mentor engineers, and collaborate with security, risk, and operations stakeholders to enable compliant AI-assisted development.

Drive outbound pipeline and qualify opportunities for an AI platform in B2B insurance. Run discovery calls, refine ICP and messaging with founders, and optimize prospecting using tools like Clay and Sales Navigator.

Design, prototype, and productize scalable machine learning solutions using LLMs. Collaborate with clients and teammates to build production-ready systems, rapidly iterate prototypes, apply statistics and ML, and implement tooling and infrastructure for deployment and model-driven features.

Analyze and redesign business processes, identify automation opportunities, and build low-code RPA workflows (UiPath, Power Automate). Collaborate with stakeholders and developers for integrations, document designs and benefits, and provide occasional after-hours support.

Lead design and delivery of production-grade AI/LLM platforms and assets to automate controls across Claims, Underwriting, Finance, and Technology. Build scalable ML pipelines, inference and serving, RAG and agentic solutions, ensure security/privacy/compliance, optimize cost and performance, and enable regional teams to operationalize AI capabilities.

The AI Engineer will design and develop scalable AI solutions for finance, ensuring compliance and collaboration across teams. Responsibilities include coding, model lifecycle management, automation, and technical mentoring.