Top AI Jobs in Toronto

The Senior .Net Developer will enhance SDG's C#/.NET Core codebase, design high-load services, and optimize MS SQL queries, benefiting from a remote work environment and attractive benefits.

Design, build, and operate cloud backend services powering connected hardware: device connectivity, telemetry ingest, state management, and data pipelines. Own features end-to-end, lead medium projects, mentor juniors, collaborate with firmware/mobile/platform teams, apply AI-native engineering practices, and participate in on-call incident response and post-mortems to ensure scalable, secure, reliable systems.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Build and operate AI-native backend services that orchestrate multi-step, stateful LLM agents for GTM workflows. Design model-agnostic abstractions, integrate external systems, deploy containerized services on Kubernetes, ensure observability, and run experiments to measure effectiveness.

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.

Lead development of a production autonomous-agent intelligence layer: implement closed-loop learning from trade outcomes, build evaluation and automated strategy generation, monitor fleet performance, manage risk and capital allocation, own model training/inference pipelines (including LLM hosting/fine-tuning), and build telemetry and data capture for real-time market adaptation.

Lead and mentor a distributed engineering team to design, build, and operate AI-enabled web products. Drive architecture, code quality, CI/CD, observability, and AI-assisted development workflows using modern full-stack technologies. Partner with Product and Design to deliver scalable APIs, cloud-native systems, and data/AI integrations while reducing technical debt and improving delivery predictability.

Test and validate Spanish (Mexican) voice AI agents, review recordings and transcripts, identify language/pronunciation and workflow issues, provide structured feedback, and support live customers via email/tickets during US Pacific Time business hours.

Lead AI-enabled financial planning and strategic analysis, build data-backed business cases, refine unit economics, deliver executive-level insights, integrate ML into FP&A, and track KPIs to drive profitability and strategic decisions.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design realistic civil litigation scenarios, draft attorney-level gold-standard responses, evaluate and grade AI-generated legal outputs using structured rubrics, provide detailed written feedback, and participate in onboarding and calibration. Minimum 20 hours/week; remote work across US, Canada, or UK.

Provide private equity expertise to evaluate and create high-quality financial and investment content for training AI systems. Review financial models, market analyses, deal documents, support due diligence, deal sourcing insights, and give structured feedback to improve analytical accuracy.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Lead integration of AI-driven tools into tax provision, planning, and multi-jurisdictional compliance. Design ML/analytics solutions for sales and payroll tax, enhance regulatory filings, support audits, and collaborate with finance, legal, and technology teams. Monitor tax regulation changes, document AI tax models for transparency and governance, and continuously refine models for scalability, reliability, and compliance.

Review and assess equity research, models, and market analyses to improve AI training data. Provide valuation, earnings forecasting, and investment memo feedback using long/short equity frameworks. Contribute to analytical materials, market sizing, and research summaries to enhance financial reasoning in foundational models.

Design and implement AI/NLP solutions to automate insurance policy administration (issuance, endorsements, renewals). Translate policy language into models and rule engines, integrate with PAS, ensure regulatory compliance, monitor model performance, and support underwriting, operations, and exception handling.

Contribute transactional and corporate law expertise to train and evaluate AI models: design realistic deal scenarios, draft attorney-quality gold-standard responses, grade AI outputs with rubrics, provide written feedback to improve model reasoning, and participate in onboarding and calibration sessions.

Analyze and classify technical content for security vulnerabilities, apply pattern recognition to improve AI model threat detection, design and evaluate security problems, and collaborate asynchronously with domain experts to produce high-quality outputs.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.