Top Python Jobs in Toronto

Owns quality for enterprise identity governance features from design through production. Responsibilities include creating test plans, automating API, integration, end-to-end, and UI testing, building performance and reliability tests, validating data integrity, integrating deterministic CI release gates, troubleshooting distributed systems, reviewing code, addressing production issues, and partnering with engineering, product, design, SRE, and customer teams.

Leads and scales Scribd’s Analytics Engineering organization, building high-performing teams and governed data foundations for AI-driven decision-making. Oversees canonical data models, semantic layers, quality, observability, documentation, security, lineage, and platform standards. Partners with engineering and business leaders to guide architecture, prioritize investments, and deliver reliable data products with measurable impact. Requires strong technical judgment, advanced SQL, modern data stack experience, cross-functional leadership, and 10+ years of relevant experience.

Provides senior technical leadership for enterprise Site Reliability Engineering, focusing on platform reliability, resilience, automation, observability, AIOps, cloud modernization, and operational excellence. The role establishes reliability objectives, leads automated remediation and infrastructure-as-code initiatives, advances monitoring and telemetry, supports transformation programs, and mentors engineers through influence rather than direct people management.

Design, build, and operate scalable data platforms covering ingestion, orchestration, modeling, quality, observability, governance, and BI enablement. Develop batch and streaming pipelines with Airflow, Snowflake, dbt, Terraform, and CI/CD tooling. Establish engineering standards, data access controls, and AI-assisted workflows while enabling software, analytics, and non-technical users to build production-grade data products. Provide technical leadership, vendor management, documentation, training, and full DevOps ownership.

Design, build, and operate production AI and automation solutions for clients. Lead discovery, solution design, proofs of concept, integrations, document processing, knowledge assistants, security controls, and ongoing optimization. Productize successful client work into packaged services, reusable accelerators, reference architectures, and repeatable delivery methods. Advise technical and executive stakeholders while helping shape the company’s AI offerings.

Develop and support full-stack Java and Angular applications, including requirements analysis, design, development, testing, implementation, security, and operational support. Collaborate with business users and technology teams, assess risks, recommend programming solutions, manage development processes, and provide technical guidance to junior analysts. The role requires independent judgment, stakeholder advising, and experience with Java, Angular, Python, SQL, CI/CD, and DevOps.

Owns production machine learning systems and automated decisioning, including graph-based models, RAG systems, real-time serving, monitoring, evaluation, drift detection, and retraining. The role establishes ML engineering and MLOps standards, shapes platform strategy, mentors scientists, and partners with senior leaders. Responsibilities include developing SignalGraph, improving LLM evaluation, applying modern deep learning methods, and communicating technical decisions and uncertainty to cross-functional stakeholders.

Leads hands-on development of pay-related SaaS features for contingent workforce management, including jobs, worker provisioning, contracts, and integrations. Designs scalable, resilient services and contributes to architecture decisions using Python, React, and AWS. Builds and maintains RESTful APIs, relational database solutions, and high-quality tested code while mentoring engineers and communicating complex technical concepts. Works effectively with ambiguous requirements in an Agile/Scrum environment.

Supports insurance pricing innovation through Earnix platform evaluation, implementation, automation, demand modeling, price optimization, advanced analytics, testing, documentation, and user adoption. Partners with actuarial, pricing, data science, and IT teams to pilot capabilities, improve pricing effectiveness and operational efficiency, and provide subject matter expertise. The role also includes coaching team members and communicating analytical recommendations.

Leads pricing and actuarial analysis for large commercial insurance accounts across Property, Casualty, and Auto products. Responsibilities include rate adequacy analysis, complex portfolio pricing reviews, alternative pricing solutions, credit risk evaluation, portfolio monitoring, and recommendations to underwriters and senior leaders. The role also drives analytical improvements and mentors actuarial analysts and students while supporting the performance and profitability of the commercial insurance business.

Own product quality for Fiscal.ai’s financial research Terminal by reproducing and fixing full-stack bugs, building Playwright end-to-end testing in CI, writing unit and integration tests, debugging frontend, backend, and data issues, and creating quality and debugging playbooks. The role requires TypeScript, React, Next.js, strong financial-data understanding, subscription and paywall testing experience, and interest in AI technologies.

Designs, engineers, and operates global DNS, DHCP, and IPAM services in a large enterprise environment. Responsibilities include DDI architecture, DNS and DHCP security, public and hybrid cloud DNS, IP and ASN management, CDN and traffic steering, network automation, troubleshooting, capacity planning, and merger or acquisition integration. The role also develops technical documentation, diagrams, scripts, and scalable solutions while collaborating with cross-functional teams and senior leadership.

Develops and supports data and AI products, translating business requirements into technical solutions. Responsibilities include data pipeline and warehouse development, testing, documentation, defect resolution, technical design, automation, reusable framework creation, process improvement, and stakeholder collaboration. The role requires ETL/ELT, SQL, NoSQL, Python, Spark, Scala, cloud, Hadoop, data profiling, batch and streaming data, and emerging AI capabilities.

Responds to production incidents across AWS and Kubernetes, diagnosing and remediating infrastructure, networking, database, cache, and deployment issues. Builds monitoring, observability, synthetic and load tests, SLOs, and reliability improvements. Maintains Terraform, Kubernetes, CI/CD, IAM, autoscaling, and security configurations; leads postmortems and incident follow-up. Participates in on-call operations and collaborates with engineering teams to improve application performance and reliability.

Develop and maintain scalable Java and big data applications across the software development lifecycle. Build distributed data-processing tools, REST APIs, and automation; analyze and debug systems; conduct test-driven validation; review code; and collaborate with internal teams and vendors. The role works with technologies including Apache Hudi, Trino, Spark, Kafka, cloud storage, and distributed computing platforms while promoting performance, reliability, modern engineering practices, and continuous technical improvement.

Develop machine learning and statistical models, analyze data, run experiments, and apply causal inference and optimization to improve Stripe’s products, risk, finance, growth, and go-to-market decisions. Partner with cross-functional teams, translate complex analyses into actionable recommendations, manage multiple projects, and help deploy and optimize production models.

Owns the Business Intelligence and Revenue Assurance programs, including data extraction, modeling, operational dashboards, reporting, rate and volume analysis, and gross-margin integrity. Analyzes large datasets to identify trends, anomalies, revenue leakage, cost opportunities, and performance improvements. Partners with stakeholders to implement controls, policies, regulatory changes, process improvements, and organizational projects across billing and operational functions.

Leads application development engineering and AI integration initiatives. Designs scalable distributed systems and microservices, drives adoption of AI-powered development tools, and guides development of agentic architectures, RAG systems, multi-agent workflows, and A2A communication. Partners with product, design, and business stakeholders while providing technical leadership, promoting agile practices, and delivering resilient software products.

Design, develop, and support cloud-native trading, analytics, and risk-management applications on Azure. Integrate market data and AI/ML capabilities, maintain CI/CD pipelines, collaborate with product and business stakeholders, and drive Agile engineering best practices to deliver scalable derivatives technology.

Develop and validate mathematical models for pharmaceutical and biologics manufacturing processes. Partner with customer subject matter experts and engineering teams to implement simulation and control solutions, contribute to product roadmaps, document research and model structures, and promote modeling best practices. The role requires bioprocess expertise, research-grade Python development, chemical engineering fundamentals, and interdisciplinary project leadership.

Build and operate AWS infrastructure, automation, observability, CI/CD pipelines, and internal developer platform components. Improve system reliability through resilience testing, automated recovery, capacity planning, and SLOs. Own projects from planning through rollout, mentor engineers, establish infrastructure standards, collaborate with software and security teams, and participate in a low-volume on-call rotation.

Own and improve Quora’s second-phase ranking models for feed and digest experiences. Build end-to-end machine learning systems covering data pipelines, candidate extraction, feature engineering, model training, and production integration. Identify new product applications for machine learning, develop efficient algorithms with other ML engineers, and contribute methodologies for large-scale recommendation systems.

Owns the evaluation and integration of PV, battery storage, inverters, power conversion systems, and LV/MV equipment for off-grid data center power plants. Responsibilities include developing system architectures, writing technical specifications and RFP scopes, evaluating vendors, modeling performance, analyzing power electronics and protection, leading factory acceptance testing and field validation, resolving integration issues, and maintaining technology roadmaps and reference designs.

Design, develop, test, and maintain scalable distributed software and data movement services for Microsoft Fabric. Investigate issues using debugging, telemetry, logs, and monitoring; participate in root cause analysis, code reviews, design documentation, automated testing, and CI/CD. Collaborate with engineers, architects, product managers, designers, and partners to deliver secure, reliable, performant features and improve customer experiences.

Design, develop, test, and maintain scalable platform software and services. Collaborate with architecture and cross-functional teams, implement platform features, participate in code reviews, document technical specifications, troubleshoot issues, and optimize performance and scalability. The role requires Go development, scripting, Kubernetes, cloud-native technologies, containerization, cloud networking, and CI/CD experience.