Top Python Jobs in Toronto

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.

Lead AppSec efforts across product, platform, and AI features by owning secure SDLC, performing threat modeling and deep code reviews, driving vulnerability management and remediation, building AppSec tooling and automations (including AI-assisted workflows), and enabling engineering teams with secure design guidance and training.

Design and build a unified analytics instrumentation platform using Go, Kubernetes, ClickHouse, and NATS. Improve data emission, transit, and quality for usage and billing, provide incident response and root cause analysis, collaborate with product and data teams to drive adoption, make tradeoffs for reliability and performance, and mentor engineers through reviews and documentation.

You will design, develop, and launch backend systems, APIs, and collaborate with cross-functional teams to support product development and operations.

Design and build scalable AWS cloud, Big Data, and machine-learning infrastructure for AI-powered Workforce Management. Lead distributed data pipeline and workflow-orchestration projects from architecture through production, improve reliability, scalability, observability, security, and cost efficiency, and develop automated testing. Collaborate across engineering, data science, product, and platform teams while influencing architecture, owning major initiatives, reviewing designs and code, and mentoring junior engineers.

Manages campaign measurement, reporting, dashboards, and analytics across marketing channels. Designs attribution, incrementality, and ROI frameworks; automates recurring reports; validates data pipelines; maintains governance and documentation; and translates complex datasets into executive insights. Partners with marketing, product, technology, risk, compliance, and finance teams to define requirements, improve reporting solutions, and recommend performance optimization strategies.

Designs, develops, tests, and supports scalable ETL applications and data engineering processes. Responsibilities include analyzing business requirements, creating data flows and automated code processes, performing impact analysis and functional testing, documenting solutions, resolving data issues, and improving development standards and process reliability. The role collaborates with technology teams and business partners in a hybrid Toronto environment, with occasional planned off-hours support.

Leads enterprise data engineering strategy and delivery, including secure data lifecycle management, cloud and hybrid data platforms, governance, integration, warehousing, streaming, and scalable pipelines. Builds ETL infrastructure and analytics tools, improves processes through automation, supports stakeholders, and advises senior leaders on complex data challenges. The role also applies AI, machine learning, MLOps, and modern data architecture practices.

Design, build, and deploy GenAI and agent-based systems for Wealth Management. Lead end-to-end solution development, integrate AI with existing platforms, enforce MLOps, model governance, security, and regulatory compliance, and collaborate with cross-functional teams to drive production-grade, scalable AI products.

Develop and scale investment-focused analytical data products, including financial datasets, ETL/ELT pipelines, dbt models, semantic layers, Power BI reports, and Streamlit applications. Partner with investment teams to translate quantitative requirements into reliable, governed, cloud-native solutions. Own products throughout their lifecycle, implementing data quality controls, orchestration, monitoring, documentation, and operational support. Contribute to AI-ready datasets and analytics modernization across portfolio management, performance, risk, trading, and research workflows.

Build and support production-grade quantitative technology solutions for investment teams. Partner with quants, researchers, traders, and portfolio managers to translate analytical workflows into reusable Python software, data pipelines, libraries, and platform capabilities. Contribute to cloud-native development, AI-enabled workflows, orchestration, monitoring, data quality, deployment, incident resolution, and platform standardization. The role requires quantitative literacy, strong software engineering, collaboration, ownership, and experience supporting analytics or investment technology environments.

Provides technical support, troubleshooting, maintenance, and development for applications, databases, ETL workflows, APIs, and internal websites. Responsibilities include database optimization, monitoring, preventive maintenance, QA, user support, documentation, reporting, data integration, JavaScript automation, and DevOps improvements. Collaborates with stakeholders and developers to resolve issues, enhance reliability, prevent fraud, and automate processes.

Develop and prototype AI applications for market and counterparty credit risk policy development and management. Collaborate with stakeholders to translate business problems into analytical solutions, conduct exploratory data analyses, identify actionable insights, and automate workflows. The role involves experimenting with LLMs, generative AI, prompt engineering, MCP integrations, machine learning, and deep analytics while communicating technical findings to non-technical stakeholders.

Creates and refines photorealistic looks for 3D assets by combining textures, shaders, surface properties, and validation lighting. Produces production-ready, lighting-ready assets, resolves technical issues, collaborates with texturing, lighting, compositing, supervision, and production teams, participates in dailies, and contributes to workflow improvements, tools, templates, and knowledge sharing.

As a Software Engineer in the Platform Team, you will build internal tooling and infrastructure to enhance productivity, collaboration, and quality across engineering teams.

Conduct advanced computer vision research in 2D/3D scene understanding, tracking, reconstruction, sensor fusion, and real-time perception. Integrate vision and language models with LLMs, optimize pipelines for embedded hardware, and develop evaluation frameworks for algorithmic and human-centered outcomes. Collaborate with engineering, hardware, and design teams to convert research into product-ready solutions and interactive prototypes.

The Director of Corporate Development will lead M&A transactions, develop investment theses, manage value creation for portfolio companies, and oversee junior team members.

Lead a team to provide production support for mission-critical Pega and AI platforms. Own incident management using observability tools, manage vendors/offshore partners, drive automation with Python/Java, improve reliability, capacity and onboarding standards, mentor engineers, and align support priorities with business and engineering stakeholders.

Hands-on Engineering Lead responsible for system architecture, backend and API design, data persistence, DevOps collaboration, performance optimization, and mentoring a 4-5 engineer team to deliver scalable, secure web applications.

Provide day-to-day support for BMO's global repo trading desks, respond to front/middle/back office inquiries, manage vendor rollouts and testing, resolve integration and settlement issues with external providers, participate in system upgrades and enhancements, and partner with business and technology stakeholders to ensure repo applications operate effectively.

As a Photonics Data Analysis Engineer, you will develop software for analyzing and measuring photonic devices, requiring advanced mathematical skills and strong programming expertise, particularly in Python and data visualization.

Lead and mentor a high-performing Platform/DevOps team, manage application delivery, and improve internal CICD platforms while ensuring reliability and observability of systems.

Design, build, and maintain automated test suites, evaluation scenarios, and CI/CD quality gates for AI-powered applications. Test APIs, validate ML model outputs, create measurable evaluation rubrics, mentor teams, and contribute to shared QA frameworks and codebases.

As a Full Stack Software Engineer, you will develop and maintain software for high traffic websites, emphasizing AI integration and quality coding practices within a cross-functional agile team.

Develop, maintain, test, and optimize scalable data pipelines and Data Lakehouse architecture on AWS. Design storage and data models, manage Redshift/RDS/DynamoDB/S3, use Glue/EMR/Lambda for processing, enforce security and compliance, optimize performance and costs, and collaborate with analysts and data scientists to enable analytics and BI.