Top Python Jobs in Toronto

Designs, builds, and operates CI/CD pipelines and automated platforms across SaaS, cloud, and on-premise environments. Responsibilities include environment provisioning and lifecycle management, monitoring and observability, incident troubleshooting, database and data operations, release engineering, infrastructure automation, and reliability improvements. The role collaborates with development, QA, product, and client teams while promoting DevOps best practices, documentation, standardization, and continuous improvement.

The AWS DevOps Engineer will manage AWS infrastructure, set up Kubernetes, build CI/CD pipelines, and automate deployments while collaborating with the team.

Build, deploy, monitor, and retrain production machine learning models for search ranking, relevance, retrieval, content extraction, classification, and LLM-driven features. Develop large-scale data pipelines from crawl and query data, analyze experiments and behavioral logs, create A/B testing and offline evaluation frameworks, and guide product launch decisions using measurable results.

Leads data architecture, integrations, product analytics, and client-specific analyses for an AI-enabled life sciences platform. Partners with engineering, product, solutions, and client services teams to build scalable, AI-ready data systems and improve measurement insights. Manages and mentors data analysts and engineers while supporting pharmaceutical and life sciences customer needs.

Own global support workforce forecasting, capacity, and scheduling models. Translate complex demand data into staffing plans, dashboards, reports, and leadership recommendations. Partner with BPO providers, execute day-to-day planning, maintain accurate reporting, identify operational improvements, and use SQL, Python, and AI tools to accelerate analysis and decision-making.

Build and maintain robotics data pipelines, curate and version large-scale sensor datasets, configure ML training workflows, optimize cloud costs, and develop tools for perception engineers. Responsibilities include dataset management, experiment reproducibility, automated evaluation, and metrics for dataset health, model performance, and pipeline reliability.

Lead API testing, test automation, and database validation for government services. Develop Playwright and UFT test suites, execute SQL-based data validation, integrate tests into CI/CD pipelines, assess performance, API security, and accessibility, and manage defect triage and resolution. Create auditable test plans, cases, and data strategies while mentoring teams and improving testing maturity.

Leads technical projects supporting energy-efficiency studies and utility programs for commercial and industrial customers. Responsibilities include supervising staff, developing scopes, budgets, algorithms, calculation tools, and savings estimates; performing energy-savings analysis and measurement and verification; evaluating HVAC, natural gas, lighting, refrigeration, and building systems; reviewing technical deliverables; and preparing reports, proposals, and presentations for utility and regulatory stakeholders.

Designs, builds, and maintains cloud environments and CI/CD pipelines for scalable, reliable applications. Responsibilities include cloud platform assessment, infrastructure automation, container orchestration, application deployment, monitoring, vulnerability remediation, security safeguards, testing, documentation, issue tracking, and technical reporting. The role requires extensive Linux, scripting, cloud, integration, testing, and DevOps tooling experience, with at least five years supporting cloud platforms and CI/CD initiatives.

Supports institutional client account management through market and portfolio analysis, performance attribution, customized reporting, presentations, and client inquiries. Builds dashboards and reporting resources, identifies workflow improvements, and helps implement AI-enabled tools, automation, and business intelligence solutions. Coordinates cross-functional initiatives, documents processes, supports change management, and trains users on new technologies. Requires financial markets knowledge, coding ability, strong analytical skills, and effective stakeholder communication.

Lead and develop a credit risk analyst team while owning underwriting, exposure decisions, portfolio monitoring, loss forecasting, reporting, and provisioning. Partner with Data Science, Risk Analytics, Finance, Product, and Support to build credit risk models, policies, controls, and automated workflows. Establish an AI-native credit risk practice for payments and future lending products, communicate portfolio trends to leadership, and support vendor evaluations and product launches.

Own go-to-market intelligence for public sector sales, including account and contact data quality, intent and propensity scoring models, ICP segmentation, competitive intelligence, and FOIA-derived insights. Translate analysis into targeting rules, prioritized account lists, territory views, and outbound workflows. Manage enrichment vendors and data ingestion, validate models against sales outcomes, and partner with Sales, Business Development, and Marketing to operationalize insights.

Design, build, test, and ship software improving service availability, scalability, latency, and efficiency. Automate operational work, define SLOs, implement observability, troubleshoot production systems across applications, Linux, and networks, lead postmortems, plan capacity and costs, document systems, and participate in shared-infrastructure incident response.

Design and build trusted commercial and financial data models, own end-to-end delivery from requirements to documentation, collaborate with commercial and finance stakeholders, apply strong SQL/dbt skills and basic Python, and use AI responsibly to accelerate analytics and foundational systems for advanced analytics.

Own and evolve a production Azure platform supporting global, compliant cloud-native applications. Responsibilities include Bicep-based infrastructure, AKS operations, identity and access management, networking, compliance audits, multi-region resilience, observability, security, cost optimization, incident response, and engineering consultation. The role also develops reusable platform modules and templates, leads technical reviews and audit responses, mentors engineers, and participates in on-call and disaster recovery activities.

Develop and test embedded software for satellite onboard systems, payloads, IoT modules, and ground equipment. Responsibilities include Embedded Linux, firmware, drivers, documentation, code and design reviews, and collaboration with electrical, digital design, and operations teams. The intern will use C/C++, OpenEmbedded/Yocto, FreeRTOS, Git, and embedded development practices while contributing to system architecture and leveraging AI-assisted engineering tools.

Embed with customers to design, build, and deploy GC AI integrations into legal workflows. Develop production-grade API/webhook integrations, troubleshoot deployments, create reference implementations and documentation, and feed product insights back to Engineering to improve the platform. Travel to customer sites up to 25% as needed.

Build and scale backend systems for Lyft’s Recommendations team, including pipelines and services that use rider context and signals to surface relevant offers. Responsibilities include system design, coding, code reviews, on-call support, project delivery, engineering best practices, mentoring, and promoting AI-assisted development. The role requires Python and Go, cloud platforms, distributed systems, databases, and strong collaboration skills, with bonus experience in ML serving, recommendation systems, feature stores, or experimentation.

The role involves developing and deploying generative AI applications using Microsoft Azure AI stack, focusing on LLMs, agent-based workflows, and scalable AI infrastructure.

Hands-on senior backend engineer role building and maintaining Python/Django services. Design, implement, test, deploy, and support backend features, optimize performance, debug across systems, work with large datasets, REST APIs, background jobs, and PostgreSQL. Collaborate with Product and Engineering and own features end-to-end.

As a Machine Learning Engineer, you will develop scalable ML systems, work with large datasets, and collaborate with a talented AI team.

Lead architecture and scaling of CI/CD infrastructure for Kubernetes/Docker microservices, drive continuous deployment strategy, integrate AI-powered developer tools, collaborate with partner teams, mentor engineers, and participate in on-call rotation to maintain platform reliability.

Design and build end-to-end features using Python and C++; architect scalable distributed systems and performant SQL schemas; implement Docker and Kubernetes workflows on AWS; design data environments and optimize queries; integrate AI agentic tools to automate development; collaborate with cloud infrastructure to maintain resilient, cloud-native applications.

Develop and scale state-of-the-art computer vision pipelines for aerial and oblique imagery. Train and improve segmentation, detection, and generative models, run large-scale PyTorch experiments, maintain modular ML infrastructure, and collaborate on deployment and inference optimizations while reproducing top-tier research.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.