Top Python Jobs in Toronto

Develop backend services and APIs using Node.js, TypeScript, Python, and AWS serverless technologies. Support FastAPI services powering LLM applications, participate in testing and code reviews, troubleshoot issues, collaborate with cross-functional teams, and learn agentic engineering practices and automation tooling under senior developer guidance.

Develop backend services and APIs using Node.js, TypeScript, Python, FastAPI, and AWS serverless technologies. Build LLM-powered applications, apply agentic engineering practices, collaborate with technical and business stakeholders, review code, support junior developers, troubleshoot complex issues, and ensure secure, scalable, reliable software delivery.

Lead design, development, and deployment of data science and GenAI solutions. Translate business goals into ML problems, engineer features, train/evaluate models and LLMs, build RAG/agent systems, work with vector DBs, mentor juniors, and communicate results to stakeholders.

Leads end-to-end analytics for marketing, digital, client, advisor, and sales data. Defines business questions, integrates data, develops dashboards and reporting, automates recurring processes, identifies trends and performance opportunities, and translates findings into recommendations. Presents insights to technical and non-technical stakeholders while supporting the transition from SAS to cloud-based Python and PySpark analytics.

Build tools and infrastructure for training language and multi-agent models in interactive environments; research and implement advances in language modeling and reinforcement learning; contribute to publications and open-source projects.

Build and improve user-facing web products across the frontend, backend, APIs, and data models. Collaborate with product and design, implement and test features, investigate production issues, improve performance and accessibility, and contribute to shared engineering systems. The role includes end-to-end ownership of product initiatives and a focus on user needs, reliability, privacy, and maintainable code.

Lead end-to-end development of customer-facing product capabilities across frontend, backend, data, integrations, infrastructure, and AI-enabled workflows. Partner with product and design, translate ambiguous user needs into usable experiences, protect privacy across roles and organizations, improve reliability and observability, review code, mentor engineers, and contribute to technical strategy and architecture.

Design, develop, deploy, and support cryptographic solutions protecting data at rest and in transit. Build Crypto as a Service capabilities, secure enterprise applications, and mainframe integrations while supporting architecture, release processes, and platform operations. Collaborate with technology teams to meet security, compliance, and resilience requirements in financial services environments.

Design, build, deploy, and maintain scalable multi-source ELT pipelines and enterprise data warehouse infrastructure. The role involves optimizing data systems for cost, performance, reliability, and SLAs; curating campaign and business data; creating monitoring and automation; and collaborating with engineering, analytics, SRE, and platform teams to deliver reliable data products.

Design, build, and operate production AI features and infrastructure for Snowflake, including model serving, inference pipelines, embeddings, RAG, and intelligent applications. Develop scalable, tested software; collaborate with researchers and product managers; contribute to architecture and technical direction; and own systems through deployment, monitoring, and operational support.

Leads strategic finance for Commerce go-to-market, partnering with revenue leaders on planning, forecasting, performance management, investment analysis, and commercial strategy. Builds driver-based financial models linking sales, marketing, pricing, adoption, transactions, and retention to outcomes. Evaluates investments using scenario and ROI analysis, prepares executive materials, and manages finance team members.

Develop and own Python-based pricing features end-to-end: design and build RESTful APIs and pricing algorithms, deploy cloud-native services, ensure observability and reliability, participate in agile processes, perform code reviews, and collaborate with product, UX, and data teams to deliver scalable production software.

Build and maintain Android applications, SDKs, frameworks, services, and operating-system components for Stripe Terminal devices. Develop payment applications across device types, collaborate with engineering, product, design, and research teams, and establish Android development best practices. The role involves creating scalable, user-focused mobile and developer experiences for in-person payments.

Designs and manages Azure infrastructure through Terraform and Infrastructure as Code, builds secure CI/CD pipelines with GitHub Actions and Azure DevOps, administers GitHub organizations, automates operational and security tasks, and supports containerized environments. The role monitors development, QA, and performance environments, enforces security and NIST-aligned practices, collaborates with engineering and QA teams, documents processes, researches tools, and mentors team members.

Provide technical direction for GitLab Dedicated, a managed single-tenant SaaS platform. Lead architecture and transformation across resilience, failover, tenant orchestration, change management, automation, and platform integrations. Identify systemic reliability and scalability risks, establish reusable platform patterns, strengthen service ownership, and guide cross-team technical decisions. Mentor senior engineers and advance engineering excellence across the organization.

Lead the design, development, delivery, and operation of secure, scalable authentication platform capabilities, including sign-in, account recovery, MFA, session management, authorization, and identity federation. Collaborate with Product, Security, Fraud, Compliance, and engineering teams; improve reliability, monitoring, code quality, and security practices; resolve production issues; mentor engineers; and communicate technical decisions and trade-offs.

Lead analytics for international market expansion, establishing scalable measurement, reporting, dashboards, data models, and operational analytics frameworks. Identify growth and profitability opportunities, define metrics, analyze performance, design experiments, and translate findings into business recommendations. Partner cross-functionally with Product, Risk, Finance, Commercial, and Operations, lead metric reviews, influence leadership decisions, and mentor junior analysts.

Implements and configures an AI platform by integrating customer data, building ETL workflows, troubleshooting pipelines, managing recurring data deliveries, and automating manual processes. The role collaborates with customers and internal teams to define technical requirements, resolve implementation issues, deploy tailored solutions, and maintain data security and privacy.

Build and operate full-stack product capabilities using React, TypeScript, backend services, APIs, cloud infrastructure, and data systems. Responsibilities include implementation, testing, deployment, monitoring, incident learning, and technical design. The role partners with product, design, and ML teams to turn life sciences data and AI capabilities into accessible customer experiences, while using coding AI agents and modern delivery practices.

Own complex customer data implementations from technical discovery through production go-live. Build automated ETL workflows, scalable data ingestion solutions, validation frameworks, and data-quality processes using Python, SQL, PySpark, Snowflake, and AWS. Troubleshoot integrations and production issues, ensure compliance with security and privacy requirements, mentor junior engineers, and improve implementation standards and tooling while working directly with technical and non-technical customer stakeholders.

Own full-stack product capabilities from problem framing through deployment and iteration. Build React and TypeScript user experiences, AWS Lambda computation services, scalable APIs, and backend systems across AWS, Kubernetes, Snowflake, PostgreSQL, DuckDB, and DataFusion. Lead technical design, code reviews, architecture discussions, testing, observability, and mentoring while partnering with product, design, ML engineering, and leadership on roadmap decisions.

Own end-to-end analytical projects supporting commercial, product, marketing, and SEO decisions across the Sports business. Build forecasting models, improve Tableau dashboards, apply statistical and behavioral analysis, strengthen data quality and pipelines, and identify growth opportunities. The role also involves stakeholder management, translating business needs into analytical solutions, influencing strategy, and mentoring team members.

Monitor and analyze security events, lead incident response and employee investigations, conduct digital forensics, communicate findings to stakeholders, build investigation tools and automated workflows, and improve security operations processes. The role requires Python scripting, SIEM experience, cloud security knowledge across AWS and GCP, and forensic analysis across macOS, Windows, and Linux.

Build and operate MITRE ATT&CK-aligned threat detections across cloud, endpoint, identity, email, and application telemetry. Own the detection lifecycle, advance detection-as-code with Git, testing, CI/CD, and observability, and improve data ingestion and enrichment. Evaluate AI-assisted security capabilities, translate threat intelligence into detections, and partner with Security Operations during investigations and incident exercises. The role requires strong Python and SQL skills, SIEM expertise, AWS and Linux knowledge, and independent project leadership.

Sets the technical vision for data science, leads complex credit risk, pricing, and loss forecasting models from conception through production, and architects scalable machine learning pipelines. Partners with senior stakeholders to shape strategy, establishes modeling and validation standards, mentors technical staff, and communicates recommendations to executives. The role requires deep fintech lending experience, advanced statistical and machine learning expertise, and hands-on production model deployment.