Top AI Jobs in Toronto

The Senior AI Solutions Engineer will architect and deploy AI systems on Google Cloud, collaborating with clients and delivery teams to create enterprise-ready solutions.

Design, build, and deploy end-to-end AI-powered applications and agentic systems. Integrate LLMs and RAG/vector search, develop APIs and UIs, operationalize models, and own CI/CD, containerization, monitoring, and reliability. Mentor engineers, contribute to architecture, and partner with product and security teams to ensure scalable, secure AI production systems.

Build and deploy high-impact AI-native products, architect agent-driven workflows, and collaborate with teams to integrate cutting-edge AI solutions into production systems.

As AI Resilience Fund Manager, you'll establish and manage a fund focused on resilient investments, lead fundraising, and advise investors on navigating challenges created by AI and economic shifts.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

Own and evolve ClickHouse's Python connectors and SDKs; build integrations with orchestration and transformation tools; design AI/LLM connector patterns for RAG, vector search, and feature pipelines; engage with open-source community; collaborate across product and engineering to prioritize and ship production-grade data integrations.

The Senior Software Engineer will develop AI/ML features, build robust APIs, create user-friendly UIs, and integrate AI models into ClickHouse Cloud.

Work directly with enterprise customers to design, build, and deploy production voice AI applications. Integrate Retell APIs/SDKs, develop conversational workflows, debug telephony/SIP and infrastructure issues, optimize AI behavior and prompts, lead technical onboarding, and drive product improvements from proof-of-concept to production.

Lead product management for AI tools in software/SaaS, manage product lifecycle, drive strategic roadmap, and ensure product adoption through iterative shipping and user feedback.

Remote contract role applying equity research expertise to train and evaluate AI models. Review AI-generated financial analyses, analyze SEC filings and financial statements, create evaluation tasks and structured datasets, and collaborate with AI researchers to improve financial reasoning and assessment criteria. Minimum 2 years equity research experience; 10 hours/week minimum commitment.

12-16 week internship implementing and optimizing GPU/accelerator kernels and custom operators, profiling and improving LLM inference and runtime performance, building benchmarks, fixing regressions, and shipping code to production or open-source AI infra projects.

Research, design, and train foundation models for robotics; align text-based reasoning with low-level control policies; evaluate zero-shot generalization; run experiments; integrate models into robotic systems and present findings externally.

Design and architect multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows and autonomous operations.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tool use, and support RLHF alignment and constitutional AI development to ensure safe AI deployment.

Lead product strategy and roadmap for AI and robotics products, translating VLA and agentic RAG breakthroughs into customer value, defining safety/usability/performance metrics, working with enterprise adopters, and driving cross-functional delivery.

Independent full-cycle recruiter managing sourcing, client engagement, offer negotiation, and placements using AI-driven tools. Acts as commercial advisor, provides market insights, and maintains self-directed billing and activity in a results-only, remote environment.

Build and operate MLOps and agentic infrastructure: manage model registries, continuous training loops, and A/B testing; deploy agents as scalable Kubernetes microservices; and create observability dashboards tracking token usage, latency, and agent reasoning.

Design, orchestrate, and optimize large-scale LLM pre-training across 1,000+ GPUs. Implement 3D parallelism, manage GPU clusters (SLURM/Kubernetes), optimize InfiniBand/RDMA networking and memory, and automate checkpointing and failure recovery for long training runs.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Optimize, compress, and distill large language and vision models for on-device inference. Build pipelines for distillation and hardware-specific compilation, and benchmark performance across NPU/GPU architectures.

Design, build, and operate end-to-end training and inference infrastructure for large language and multimodal models. Improve efficiency (memory, parallelism, kernel optimizations), ensure robust scalable training and RL pipelines, optimize low-latency/high-throughput serving (quantization, caching, speculative decoding), manage multi-GPU and multi-cloud orchestration, and productionize new algorithms with strong observability and reproducibility.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As the AI Agents Solutions Architect at Kraken, you'll design and build an AI-native finance operating system, automating finance operations and ensuring compliance in a regulated environment.

Build production-grade AI agent systems, automations, and workflows across recruiting, security/compliance, finance, onboarding, CRM integrations, and growth. Implement LLM-powered solutions, API integrations, and operational tooling to improve hiring, sales, and enterprise readiness.