Top Python Jobs in Toronto

As a QA Engineer, you will ensure app quality and performance, develop automated tests, and identify bugs while collaborating with cross-functional teams.

Design and implement complex agentic workflows, ensuring reliable integration with the Guidewire ecosystem, while mentoring engineers and enhancing innovation in AI applications.

The Principal Software Engineer will design and build scalable platform services for the authorization platform in a healthcare context, mentor team members, and drive architectural decisions.

This role provides senior-level HRIS support, managing SAP SuccessFactors operations, integrations, testing, and data management while assisting HR in system enhancements and transformations.

As a Backend Engineer, you'll build APIs, lead product development processes, and contribute to distributed systems while leveraging AI technology for improvements.

Lead the design and development of AI services, managing technical teams to create enterprise-wide Generative AI platforms and solutions.

The QA Analyst / Automation Tester will create test cases, develop automation scripts, validate data, track defects, and support UAT and releases.

The Actuarial Consultant leads pricing initiatives and projects, collaborates with partners, develops analytical models, and coaches team members to enhance operational efficiency and profitability.

The Business Operations & Strategy Lead will enhance operational efficiency, implement AI-driven solutions, coordinate cross-functional teams, and support strategic initiatives at ZestyAI.

The Big Data Engineer will design and maintain data pipelines, manage AWS infrastructure, optimize ETL processes, and support operational activities while mentoring junior staff.

The Senior QA Analyst will build automation frameworks using Python, validate data pipelines in AWS, and conduct deep-dive SQL analyses to ensure data integrity and system performance in a trading environment.

The DevOps Engineer role involves supporting infrastructure operations, resolving deployment issues, maintaining CI/CD pipelines, and collaborating on continuous improvement initiatives.

The role involves architecting deep neural networks for object detection and tracking, optimizing performance, and developing data strategies in a low-data environment.

As an Applied Researcher, you will develop machine learning and NLP solutions to enhance eBay's query understanding and search products, collaborating with cross-functional teams and applying cutting-edge techniques in e-commerce search.

The Principal Machine Learning Architect will design and implement data-centric ML solutions, ensuring high-quality ML model development and deployment strategies for a leading ad tech company.

As the GTM Insights & Analytics Lead, you will build BI reporting infrastructure for Sales, Marketing, and Finance, maintain the GTM data dictionary, lead revenue forecasting, and perform analytical projects connecting data across functions.

Lead the design and development of a data platform, integrating data sources, driving architecture migration, and ensuring robust data governance and API development.

The Senior ML/RL Engineer will develop behavior models, implement RL algorithms focusing on safety, design reward functions and optimize training environments while collaborating across teams.

The Engineering Manager will lead cross-functional teams of engineers, focusing on building high-performing environments, managing technical delivery, fostering team growth, and ensuring data-driven strategies.

The AI Architect will lead technical execution of AI strategies, design autonomous agent systems, and guide clients in deploying Generative AI solutions.

The Software Engineer II will design and implement core components of a new SMTP relay harness and own the mail flow services, collaborating with teams to meet production requirements.

Design, deploy, and operate large-scale distributed systems. Operate Kubernetes clusters and Istio, automate workflows with Go/Python/shell, build observability with Prometheus/Grafana/Loki, troubleshoot networking/storage/performance, support AI/ML infrastructure, and participate in on-call rotations and postmortems to improve reliability.

Lead data-driven underwriting improvements by analyzing claims, risk signals, and underwriting data; build statistical and ML models for cyber risk assessment, pricing, and automation; produce dashboards and reports; collaborate with actuarial, product, and engineering teams; mentor teammates and lead cross-functional initiatives to optimize underwriting and risk selection.

Design, build, and operate ML infrastructure powering data, compute, artifacts, and orchestration across cloud and on-prem. Own backend services, storage, observability, security, and developer tools; collaborate with cloud/compute providers and lead reliability and scaling efforts.

Lead design and production deployment of an agentic AI platform for a cloud-native EHS/ESG SaaS. Build multi-agent state machines, scalable RAG pipelines, secure multi-tenant architectures, automated PII redaction, and observability for LLM routing, token cost control, and real-world latency/robustness. Collaborate with frontend teams to deliver dynamic widget-rendering APIs and serverless ingestion pipelines.