Top Python Jobs in Toronto

Design, build, test, and deploy production-grade data pipelines, APIs, and distributed PySpark workloads on Microsoft Fabric and Azure. Use Python, dbt, Airflow, and Docker to package and orchestrate workloads, implement data quality, observability, lineage, and governance (Purview), and collaborate with governance, analytics, and IT teams to deliver scalable, secure, production-ready data platform solutions.

Lead design and delivery of the integrations platform supporting 250+ integrations. Build APIs, integrations, tooling, and observability; engage customers; optimize performance and scalability; set architectural direction and best practices.

Lead design and implementation of a scalable data platform handling petabytes of telematics and time-series data. Build and optimize streaming and batch pipelines, design storage solutions, implement observability and data lineage, manage HA infrastructure, and write maintainable Java/Python code while shaping long-term architectural strategy.

Support and enhance Aviva Canada's Economic Capital internal model via stochastic simulation, quantitative risk analysis, and model validation. Build and maintain statistical and catastrophe modelling frameworks, analyze complex actuarial datasets, inform capital metrics and allocation, and communicate technical findings to stakeholders. Contribute to climate risk (OSFI B-15) physical risk modelling and model governance.

Lead architecture and build an internal, scalable evaluation platform for LLMs and agentic systems. Own orchestration, automation, and observability to turn manual prompt iteration into reproducible evaluation loops. Act as AI technical lead for squads, define quality metrics, mentor engineers, and collaborate with infrastructure teams to run large-scale evaluations and agent simulations.

Design, build, and maintain CI/CD pipelines, developer platform tooling, and IaC for a large Nx monorepo. Improve release safety, observability, and reliability; support Kubernetes deployments, GitOps flows, and platform security; coach teams on DevOps best practices and developer experience.

Develop and maintain InstaLILY's AI agents platform, collaborate with product/design/growth and enterprise stakeholders, ship customer-facing features, ensure reliability via testing and CI/CD, and iterate on feedback while staying current on AI trends.

Lead the engineering team for a search platform at Thomson Reuters, focusing on building, operating, and evolving the infrastructure while managing a team of software engineers and aligning efforts with business priorities.

The Senior Full-Stack Engineer for AI Platform will design and implement frontend and backend components, ensuring high-quality code, system performance, and collaboration across teams. The role emphasizes delivery, architecture, and testing discipline in a fast-paced environment.

The Senior Backend Engineer will design and implement backend services using TypeScript and Python, focusing on NestJS and FastAPI, and contribute to AI-related workflows and document processing.

Build production-grade UiPath automations and Claude-powered AI agent workflows, integrate with enterprise systems (APIs, ERPs, CRMs), write custom Python/JS/C# components, create reusable libraries, run unit tests and UAT, and participate in two-week agile sprints to deliver and stabilize automation solutions.

As an AI/ML DevOps Engineer, you will operationalize AI solutions, build CI/CD pipelines, and ensure production system reliability. Collaborate with data scientists and engineers to deliver AI capabilities and maintain strong operational practices.

Lead investment data governance by designing and operating data quality controls, reconciling and troubleshooting investment system data, administering reference/security master and pricing data, managing vendors, supporting front-office/trading applications, and participating in system releases and MDM activities to ensure accurate, timely data for investment decision-making.

Senior backend engineer responsible for building and owning APIs and partner integrations, optimizing database and service performance, debugging production issues using observability (logs, metrics, traces), mentoring engineers, and guiding architecture and scalability decisions while shipping reliable Python/Django services.

The Machine Learning Engineer II crafts and deploys advanced AI solutions leveraging machine learning technologies, focusing on real-world applications and scalable systems. Responsibilities include coding for model deployment, managing large datasets, and working within a collaborative AI team.

Lead design and implementation of scalable data models, ETL/ELT pipelines and migrations to Snowflake/Databricks. Optimize data workflows, ensure data quality and governance, support analytics reporting, and provide technical leadership to junior engineers while collaborating with stakeholders to deliver data-driven solutions.

Join a cross-functional team to develop data products, write efficient code, influence AI roadmap, and collaborate with diverse professionals.

The role involves designing and developing front-end and back-end components, mentoring team members, ensuring system efficiency, and optimizing platform performance in a collaborative team environment.

Design and implement scalable AI agent pipelines and runtimes, build automated code-generation and self-debugging loops, create advanced agent memory systems, develop automated evaluation frameworks for agent accuracy and latency, and translate cutting-edge agent research into production-grade software libraries.

Conduct research on safe, reliable embodied AI for robotic manipulation and autonomous decision-making. Develop and evaluate reinforcement learning, imitation learning, and learning-from-demonstration methods; leverage negative data, human feedback, and corrective demonstrations; design and train Vision-Language-Action and Vision-Language models; implement and test models in simulation and on real robotic platforms; collaborate to translate research into scalable prototypes and publish results.

Lead and grow the hardware test engineering team to deliver test infrastructure, procedures, automation, and evidence packages for satellite hardware. Oversee validation, qualification, production test strategy, reliability campaigns, cross-functional alignment, and scalable processes while coaching engineers and advancing test automation, data systems, and facility capability.

The Senior Software Engineer will architect and deliver a CRM and data platform, automate workflows, and integrate data across systems to support growth in the firm.

Design, implement, and automate test procedures, fixtures, and software for PCBs, subassemblies, and systems. Develop test plans, execute prototype bring-up and debug, perform root-cause analysis, provide DFT feedback, and collaborate with electrical, firmware, mechanical, and manufacturing teams to ensure product reliability and testability.

Design AI algorithms for various applications in site-monitoring, maintain and improve services, and collaborate effectively with the team.

Design, implement, and maintain high-performance SBT Content microservices for P&C insurance. Collaborate in agile teams, apply AI-assisted coding, ensure test coverage, CI/CD, observability, and scalable cloud deployments.