Top Python Jobs in Toronto

Build and evolve product features across backend and frontend technologies. Collaborate with Product and Design teams to improve user experiences and take ownership of features in production.

The Product Engineer will design and build features using Python, TypeScript, and React, ensuring high-quality outcomes through customer engagement and end-to-end ownership of projects.

Manage, support, and modernize high-volume relational database infrastructure. Lead SQL Server issue resolution, performance tuning, HA/DR design, backup validation, automation (DB-as-Code), monitoring, and Level 2 operational/project support, including 24/7 on-call participation and cross-team vendor collaboration.

The Senior Guidewire PolicyCenter Developer designs, configures, and develops PolicyCenter solutions, ensuring best practices in coding and deployment while mentoring the team.

Design, build, test, and maintain Magnet One cloud platform features across front-end and back-end. Collaborate on architecture, perform code reviews, troubleshoot defects, and drive best practices for secure, performant, and maintainable software.

Partner with sales and customer technical teams to design and deliver scalable Databricks Lakehouse architectures, present reference architectures and demos, validate integrations, and drive adoption of data engineering, data science, and ML solutions. Serve as a trusted technical advisor throughout the sales lifecycle and manage delivery of production data solutions.

Design, automate, and operate hybrid cloud and datacenter networks. Develop and maintain Ansible playbooks and automation, troubleshoot connectivity, implement security, monitor systems, and participate in 24/7 on-call escalation for highly available network architectures.

Build and evolve scalable observability systems: high-throughput telemetry ingest, cost-efficient storage, instrumentation libraries, SLO/alerting infrastructure, and integrations to improve reliability, diagnostics, and incident response across multi-cloud infrastructure.

The Senior Manager of Data Engineering leads a team in developing data and AI products, guiding architecture and design, and ensuring process improvements while managing stakeholder expectations and technical implementation.

Lead underwater acoustics modelling and assessments for coastal and offshore projects, develop sound propagation and exposure models, characterize sources, analyze monitoring data, design monitoring programs, produce technical reports, advise on mitigation and compliance, mentor junior staff, and support proposals and business development.

Lead technical strategy and hands-on implementation of Autodesks cloud platform and Internal Developer Platform. Architect AWS/EKS-based, IaC and GitOps-driven platforms, improve developer productivity via self-service paved roads, drive observability, SRE and security-by-default, and integrate AI-enabled platform services (LLM APIs, RAG, agentic workflows). Provide architecture reviews, mentoring, and cross-functional technical leadership.

Source and recruit technical and non-technical talent globally, build pipelines for high-priority roles, conduct personalized outbound campaigns, and partner with hiring managers through offer closure. The role requires strong Boolean, X-Ray, GitHub, and passive-candidate sourcing skills, along with recruiting analytics, AI and automation use, coordination, and independent management of multiple hiring priorities.

Participate in structured SAP training covering S/4HANA, business processes, configuration concepts, testing, documentation, and troubleshooting. Assist senior consultants with requirements gathering, issue tracking, process analysis, and project scenarios. Develop functional or technical expertise across SAP areas such as FICO, MM, SD, ABAP, Basis, BTP, and SuccessFactors, with mentorship and preparation for entry-level SAP consulting roles.

Build and deploy AI-powered automations across the business. Partner with teams to identify opportunities, redesign processes, integrate systems through APIs, and launch scalable workflows using LLMs, agents, and automation platforms. Document solutions, establish safeguards, enable adoption, and measure efficiency gains. The role requires strong ownership, responsible AI practices, business understanding, and hands-on experience with AI development, APIs, Python or similar languages, SQL, databases, and modern deployment tools.

Analyze and optimize payment-risk rules, thresholds, customer segments, and manual-review strategies. Use SQL, Python, experimentation, and decision-science techniques to reduce false positives, customer friction, operational workload, and payment losses. Investigate fraud patterns, build monitoring and reporting, evaluate risk changes, and provide practical recommendations while partnering with Risk Operations, Product, Engineering, and Data teams.

Lead QA activities for a healthcare data modernization program: design and execute automated UI and API tests, validate HL7 FHIR R4 resources and v2-to-FHIR mappings, build and maintain automation frameworks, manage defects in Jira, collaborate with cross-functional teams, support CI/CD and DevOps testing, and mentor QA team members.

Design, build, and maintain integrations and custom applications across CRM, marketing automation, analytics, and CDP platforms. Implement and configure Adobe and other MarTech tools, work with APIs and data sources to enable personalization and measurement, participate in code reviews and testing, and collaborate with cross-functional teams and clients.

Build and enhance actuarial applications using R Shiny; design responsive UIs with JavaScript/HTML/CSS; create reusable JavaScript components; refactor R code; collaborate with actuaries to translate requirements; build UI test suites and follow software engineering best practices.

Designs and maintains scalable cloud and on-premise infrastructure, Infrastructure as Code, Kubernetes environments, CI/CD pipelines, data streaming, caching, observability, and incident response systems. The role owns AWS reliability and cost efficiency, supports progressive deployments, troubleshoots complex production issues, partners with software teams, and drives automation and continuous improvement across platform operations.

Bridge frontier multimodal research and product by shaping research priorities, interpreting evaluations, building prototypes, and defining research-to-product handoffs across Canvas, Agents, and the model platform to deliver enterprise-facing features.

Work across modeling, data, systems, and evaluation to make video foundation models expressive, controllable, and personalized. Build adaptation and personalization pipelines, define end-user quality metrics and evaluations, and collaborate with product and design to deploy production-ready model variants for creative partners.

Provide fourth-level IP network support and debugging for escalated customer issues, coordinate with R&D and support teams, perform on-site adaptations, maintain problem records and release notes, and support product reliability and test environments across global teams.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.