Top Python Jobs in Toronto

Develop and maintain automated and functional tests covering API, UI, integration, system, and regression scenarios. Integrate scalable test automation into CI/CD pipelines, collaborate with engineering and product teams, provide test feedback, and contribute to technical planning and code reviews. The role also involves testing edge cases, compatibility, concurrency, backend systems, and large relational datasets using SQL.

Build and maintain Python automation tools, test frameworks, and Jenkins CI/CD pipelines supporting functional, regression, and performance testing. Investigate test and pipeline failures, validate software fixes, improve automation coverage and reliability, and communicate actionable results. Partner with QA, Engineering, and DevOps teams on tooling and enterprise changes. Provide technical guidance, mentor co-op students, support hiring, and report on team progress, risks, priorities, and initiatives.

Own the architecture, scalability, security, reliability, and technical direction of critical AWS-based platform domains. Design distributed, event-driven backend services, data models, APIs, multi-tenant systems, and AI/ML infrastructure. Lead cross-team initiatives from design through release, establish engineering standards, mentor engineers, improve observability and delivery practices, and guide modernization while maintaining product quality and cost efficiency.

Lead design and implementation of production AI/LLM systems and scalable FastAPI backends, implement embedding-based semantic search with PostgreSQL/pgvector, build WebSocket real-time collaboration, own GCP/Kubernetes infrastructure (KEDA, Terraform), and mentor engineers while driving architectural decisions and feature rollouts.

Develop and maintain full-stack or backend enterprise software for multi-tenancy, linked accounts, policy management, and administrative experiences. Build and test production systems while helping migrate from a monolith to microservices. Collaborate across teams, contribute to architecture and documentation, review code, mentor junior developers, plan technical work, and participate in on-call support.

Leads and develops software engineering teams while remaining hands-on with coding, architecture, code reviews, and technical solution design. Partners with product and design leadership to identify opportunities and create adaptable roadmaps. Coaches engineers, supports career growth, provides actionable feedback, participates in hiring, and helps build high-performing teams developing large-scale SaaS products.

Own budgeting, forecasting, reporting, financial modeling, and strategic analysis across the company. Partner with cross-functional leaders to drive decisions, operational efficiency, revenue growth, and cost reduction. Manage and evolve the Pigment financial planning platform, automate FP&A processes, track KPIs, and present financial insights to leadership and the Board.

Analyze business and product data to support marketing initiatives, product development, forecasting, experimentation, reporting, and personalized recommendations. Build and optimize SQL and dbt data models, create Looker-based insights, monitor KPIs, design statistical experiments, and translate complex findings into actionable recommendations for technical and non-technical stakeholders. Partner closely with a Product Manager and cross-functional product team.

Lead functional, regression, integration, system, and end-to-end testing for complex banking applications. Validate databases, APIs, batch workflows, event-driven systems, ETL pipelines, integrations, and data transformations. Create comprehensive test documentation, develop automated tests, analyze defects, manage testing risks, and communicate release quality. Collaborate with engineering and product teams, improve QA processes, and mentor junior QA engineers.

Design, configure, develop, test, deploy, troubleshoot, and support Zafin SaaS solutions for financial institutions. Translate business and banking requirements into technical designs, integrations, APIs, workflows, data mappings, and implementation plans. Lead technical workstreams, client workshops, testing, defect resolution, deployment readiness, and production support. Provide technical direction, mentor junior consultants, manage delivery risks, and ensure solutions are scalable, secure, auditable, maintainable, and operationally ready.

Conduct original research on reliable agentic AI, developing methods for simulation, evaluation, optimization, regression control, and continuous learning. Analyze agent failures, traces, and human feedback; create experiments and prototypes; and translate research into production-facing systems. Collaborate with product and engineering teams, contribute to technical strategy, and provide rigorous experimental evidence. The role requires strong research depth, publication or open-source contributions, hands-on Python development, and expertise in LLM agents or agentic systems.

Own the company’s AI-powered quality and release infrastructure as its first dedicated hire. Build agent-run test harnesses, LLM graders, regression coverage, release gates, fresh-install and upgrade verification, and natural-language UAT. Manage CI health, artifact publication, deployment pipelines, credentials, certificates, and staging environments. Write production Python code, convert escaped bugs into regression tests, and provide trusted release-readiness evidence through dashboards, runbooks, retrospectives, and operating policies.

Build prototypes for multimodal AI and human-machine interaction on real devices. Integrate speech, vision, language, and multimodal models; implement interactions combining voice, touch, gesture, motion, and sensor inputs; and evaluate on-device versus cloud AI trade-offs. Collaborate with researchers and designers, iterate based on experiments, debug implementations, and use ML frameworks for inference, fine-tuning, and evaluation.

Lead research in computer vision, spatial perception, and human-centered AI. Develop real-time 2D/3D vision, tracking, gesture recognition, compact model, and agentic AI systems. Integrate foundation models with efficient perception pipelines, optimize for hardware constraints, establish datasets and evaluation metrics, build prototypes, support product integration, and mentor researchers and engineers.

Designs, builds, tests, and evolves enterprise AI agents, workflows, services, and applications. Owns technical decisions, architecture, scalability, security, responsible AI guardrails, and technical debt. Collaborates with product, UX, and engineering teams, conducts code reviews, communicates designs, and mentors junior engineers. Requires strong experience with production AI agents, LLMs, RAG, prompt engineering, orchestration frameworks, and software development, with preferred expertise in C#, .NET, Python, containers, cloud deployment, and scalable APIs.

Software engineering interns develop and test customer-facing infotainment, mobile, connectivity, and automotive applications. Responsibilities may include Android Automotive development, platform software, wireless and telematics integration, 3D graphics, automation testing, debugging, and presenting project progress. Interns collaborate with product and design teams throughout a full-time January-to-August 2027 internship.

Partner with Marketing, Sales, Operations, Strategy, and other teams to define analytical needs and deliver insights. Analyze customer behavior, product usage, engagement, retention, marketing performance, and operational outcomes using SQL, Python, Snowflake, visualization tools, and AI. Build dashboards and reports, identify trends and opportunities, and communicate practical recommendations to technical and nontechnical stakeholders.

Provides technical leadership and hands-on software engineering for Autodesk’s developer portal and application management platform. Responsibilities include architecture, implementation, API and workflow improvements, production operations, engineering excellence, AI-assisted development, cross-team technical direction, and mentoring. The role requires designing scalable distributed systems, resolving complex production issues, influencing decisions across teams, and delivering reliable developer-facing products.

Own risk strategy and operating models for new credit and payroll products. Lead transaction monitoring operations, people management, staffing, service levels, risk rules, controls, workflows, and performance metrics. Partner with Product, Engineering, Data, Compliance, and Customer Experience teams to identify risks, analyze transaction data, investigate failures, and improve decision quality. Build scalable programs from zero to one while balancing loss prevention, customer experience, and operational efficiency.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Perform lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to validate data assets. Implement advanced SQL and PySpark pipelines, orchestration, data quality, observability, lineage, masking, and row- and column-level security. The role also supports vector search, embeddings, cloud data onboarding, and regulated-domain use cases.

Design, build, and maintain People systems automations and integrations (Workato, Workday, Greenhouse, Slack) using production-quality code. Improve CI/CD, reliability, observability, and AI-driven automation (Claude, MCP). Serve as technical lead for People Technology, partner with stakeholders, and drive scalable, maintainable automation solutions.

Manage analytics consulting projects from problem definition through solution delivery. Translate client business challenges into analytics designs, apply statistical and machine learning techniques, present insights to executives, and develop plans to operationalize solutions. Collaborate with global consulting, IT, and data engineering teams while managing stakeholders and project teams.

Prototype, evaluate, and productionize machine learning systems for restaurant content understanding, retrieval, ranking, matching, and recommendations. Build scalable data and ML pipelines, design experiments and evaluation frameworks, apply LLMs and multimodal methods, and develop reusable tooling. The role requires collaboration with engineering, product, and content teams to translate business needs into reliable, scalable ML solutions.

Lead digital media campaign measurement, performance reporting, dashboard development, data integration, quality monitoring, and reporting automation. Analyze incrementality tests, statistical models, attribution, and market trends to identify media impact and provide actionable recommendations. Collaborate with data engineering, agency, client, and executive stakeholders, presenting complex findings in clear business language and maintaining reporting standards and documentation.

Lead digital media campaign measurement, performance reporting, dashboard development, and data management. Build reporting infrastructure, maintain media-platform data connectors, monitor data quality, and automate reporting workflows. Conduct or support incrementality testing, statistical modeling, and causal analysis to evaluate media effectiveness. Translate complex findings into clear business recommendations for clients and internal teams while collaborating with data engineering, agency, and client stakeholders.