Top Python Jobs in Toronto

Responsible for building robust infrastructure to support a development pipeline, ensuring quality gates are in place, and optimizing tools for efficiency. Requires collaboration with various teams to improve deployment and validation processes.

Design and implement CIAM backend services for registration, authentication, and account management across B2C and B2B platforms, utilizing security standards and automation practices.

As a Sr. Data Engineer, you'll design and manage data pipelines, enhance data quality, and leverage AI to streamline operations, driving the success of our AI growth platform for real estate.

Lead design, development, testing, implementation and support of data and AI products. Translate business requirements, provide technical direction, mentor developers, build automation and frameworks, improve delivery processes, and perform data analysis, profiling and regression testing.

Support design, development, and maintenance of Power BI dashboards and data models. Collaborate with stakeholders and data engineers to integrate data sources, ensure data quality, monitor refreshes, optimize performance, document logic, and deliver metrics and forecasting support for fundraising and operations.

Conduct quantitative and qualitative economic and market research to support OSC policy and regulatory work. Design studies, collect and analyze data using relational databases and modern data platforms, apply Python/R and ML techniques, and communicate findings via reports and presentations to stakeholders and senior leadership.

Partner with Product and Marketing to drive analytics strategy: design and analyze A/B tests, build predictive models, transform large datasets with SQL and Python/R, integrate generative AI to accelerate workflows, create automated dashboards, translate insights for stakeholders, and mentor junior analysts.

Lead development, verification, and optimization of statistical and machine learning models for retail credit risk. Architect end-to-end Databricks model pipelines, implement production strategies using tools like FICO Decision Optimizer, Python, and SAS, and apply MLOps best practices. Ensure model governance, validation documentation, performance monitoring, and clear communication of results to technical and non-technical stakeholders while collaborating across risk, credit decisioning, and validation teams.

Design, build, and maintain enterprise CI/CD pipelines (GitHub Actions) and secure Databricks pipelines for ML model training, validation, and deployment. Integrate security tooling (SAST/DAST, Snyk, Palo Alto AIRS, dependency/container scanning), implement policy-as-code guardrails, and improve pipeline reliability, observability, and performance. Collaborate with Security, ML Platform, and Infrastructure teams and produce documentation and reusable pipeline templates.

Design, build, and maintain enterprise MLOps pipelines and CI/CD for data ingestion, training, validation, and deployment. Ensure compliance, monitoring, audit readiness, and integration with production systems. Collaborate with data engineers, DevOps, and product owners, drive platform improvements, and mentor junior team members.

Design, build, and support Adobe Experience Cloud solutions (AEP, AJO, CJA) including data pipelines, XDM schema design, identity resolution, integrations, journey orchestration, monitoring, governance, and cross-functional delivery to enable personalization, activation, and analytics.

The Senior Software Engineer, AI will develop and maintain AI/ML features, implement models, and collaborate on innovative solutions within a cross-functional team.

Design, build, and support Adobe Experience Cloud data and activation solutions. Implement RTCDP, XDM schema, data pipelines (batch/real-time), AJO journeys, CJA reporting, integrations with CRM/warehouses/SDKs, and platform governance. Troubleshoot ingestion, identity resolution, and support production operations while collaborating with cross-functional teams on design, testing, and deployments.

Design, build, and deploy production-grade machine learning systems and scalable ML frameworks. Work with large, multi-modal financial datasets, write maintainable code, and collaborate on deep learning, generative AI, and forecasting solutions while promoting reliability and developer productivity.

Monitor and analyze wholesale liquidity metrics (LCR, NCCF, NSFR); support forecasting and liquidity models; build analytics and dashboards; apply data science and statistical techniques; prepare senior management reporting; support regulatory submissions and policy reviews; collaborate with Treasury, Risk, and business stakeholders.

Implement liquidity risk frameworks and systems for wholesale banking. Translate methodologies into business requirements, validate UAT outputs, assess data/systems, collaborate with Technology, Risk, Finance and Front Office, and support regulatory and stress-testing reporting and enhancements.

Designs and maintains scalable data pipelines and cloud data platforms, implements ETL/ELT and warehousing solutions, supports batch and streaming data, applies data governance and quality, integrates AI/GenAI capabilities, and collaborates with stakeholders to deliver analytics and data-driven insights.

Build and maintain full-stack web applications and production LLM-powered features (RAG, agents). Develop evals, observability, API integrations, databases, background jobs, containerized deployments, and collaborate across product, design, and engineering to ship AI products.

As an SRE Observability Architect, you'll define enterprise observability architecture, lead the design of platforms, and standardize tooling while collaborating with various engineering teams.

Build, own, and evolve Flinks' data and ML platform: BigQuery/dbt pipelines, Airflow ingestion, Kubeflow/Vertex training, model serving, governance, observability, and cross-team data contracts to support production ML and analytics at scale.

Lead the MLOps team focused on AWS cloud services and machine learning operations, design production-grade capabilities, and mentor other engineers.

Own and ship production ML models end-to-end: problem framing, model design, training pipelines, deployment to served endpoints, and model-quality monitoring. Design experiments, detect drift, run champion-challenger evaluations, and partner with Data Engineering, backend, product and QA to integrate model outputs that drive business outcomes in fintech.

Design, develop, and maintain Linux-based software, automate workflows, and debug code while collaborating with cross-functional teams.

As a Staff Full Stack Software Engineer, you will design and build foundational infrastructure, develop enterprise features, and ensure system reliability handling sensitive legal data.

As an Engineering Manager at Harvey, you will lead a full-stack team, oversee core product delivery, and ensure high-quality user experiences by combining technical design and product vision.