Top Python Jobs in Toronto

Lead design and delivery of cloud-native, secure, scalable data catalog services. Drive architecture, hands-on full‑stack development, CI/CD/DevOps, AI-assisted workflows, security, observability, and team enablement across enterprise platforms.

Provide 24/7 Level 2 operational support for Azure public cloud platforms (PaaS/IaaS/Containers), manage major incidents, perform root cause analysis, oversee preventive maintenance and vendor escalations, produce support documentation and scripts, and ensure ITIL-aligned change/incident/problem management and compliance.

Senior technical leader owning enterprise performance engineering strategy and QE tooling administration. Defines non-functional testing models, integrates performance tests into CI/CD, analyzes results vs SLAs, guides architecture and capacity planning, mentors QE teams, and manages qTest, BlazeMeter, and Sauce Labs governance, reporting, and stability.

Work with sales and technical contacts to architect and demo voice AI solutions, create enablement and self-service tools, scale integrations, and productize common patterns while supporting enterprise customer deployments.

Work with customers and cross-functional teams to identify high-impact AI agent use cases, drive end-to-end deal delivery, prototype and demo solutions, guide production readiness, embed with strategic accounts, and feed field insights to shape product and model roadmaps.

Lead enterprise performance engineering strategy and QE tooling administration. Define non-functional test models, integrate performance testing into CI/CD, analyze results vs. SLAs, govern qTest/BlazeMeter/Sauce Labs, mentor QEs, and ensure tool stability, scalability, and cost-effectiveness across portfolios.

Develop and maintain cloud-enabled data catalog services and components. Implement features, write maintainable code, perform unit/component testing, support deployments, follow secure cloud and architecture patterns, participate in peer reviews and production support, and document implementations under guidance from senior engineers.

Lead TDAF Commercial data strategy and reporting excellence. Design centralized data architecture, ensure data integrity and governance, build scalable reporting/analytics, drive policy alignment, and influence senior stakeholders through data storytelling and cross-functional partnership.

Design, build, test and support cloud-native data catalog services and APIs. Translate requirements into technical designs, implement secure, scalable components, automate CI/CD and testing, and support operational readiness and incident resolution while following enterprise engineering and AI-assisted development guardrails.

Manage development, validation, and documentation of Canadian consumer credit risk reports. Maintain reporting framework, improve automation, design regular and ad-hoc reports for management and regulators, ensure data governance and controls, support audits, advise business stakeholders, and coordinate databases and analytics work.

Design, build, and deploy scalable AI features using LLMs, knowledge graphs, and orchestration frameworks. Integrate models into enterprise products, optimize performance and latency, and manage model lifecycle, observability, and cloud-native deployments. Collaborate cross-functionally, lead engineering workstreams, and maintain high quality, secure production services.

Lead analysis of customer, digital, and transportation data to build targeting, pricing, and optimization models. Design and run customer analytics (price elasticity, churn, LTV, segmentation), mentor junior data scientists, lead stakeholder projects, and support data storage planning and cross-department representation to drive actionable, data-driven business decisions.

Lead design and delivery of scalable, governed data ingestion and integration pipelines for structured, semi-structured, and multimodal data to enable AI, ML, and Generative AI. Partner with product, data science, architecture, risk, and operations to define requirements, ensure data quality, lineage, security, and observability, and evaluate new technologies. Mentor engineers, drive cross-functional initiatives, improve platform resilience, and measure ingestion and AI data effectiveness.

Design, build and maintain Azure-based data ingestion, ETL and curation pipelines (batch and real-time). Collaborate with stakeholders, prototype solutions, ensure data quality and compliance, automate/monitor analytics services, provide L3 production support, and follow agile practices with code reviews and CI/CD.

Design, validate, and deploy publication-grade predictive and causal models for commercial and patient outcomes in rare disease. Build survival and time-series models, Bayesian marketing-mix and next-best-action systems, integrate Snowflake Cortex AI and AI agents, ensure HIPAA-compliant data operations, implement interpretability and bias guardrails, and translate insights to commercial stakeholders.

Design, build, and operate the AI platform powering conversational agents and large-scale document workflows. Own orchestration, retrieval, inference routing, observability, evaluation, and reliability for scalable, cost-sensitive LLM systems. Define APIs, data contracts, quality gates, and governance; optimize performance and cost; mentor engineers and lead technical decisions across distributed teams.

Provide analytics support to business lines and finance through data analysis, automated workflows, BI reporting, advanced analytics and generative AI. Engage stakeholders, present findings to senior leaders, improve processes, and implement cloud-based data solutions.

Support Prime Brokerage, Electronic Trading, and Clearing Services by processing trades, allocations, reconciliations, price/security master maintenance, fee calculations, reporting, and troubleshooting between front and back office. Use Python, SQL, VBA, and Excel to automate tasks, improve processes, and support new product initiatives. Liaise with traders, clients, technology, and senior management; analyze and recommend operational enhancements.

The Lead AI Forward Engineer designs AI solutions, guiding implementations for scalability and compliance, while mentoring teams and shaping technology strategy.

Design, deploy, and operate secure, automated cloud infrastructure (primarily AWS) and networking. Troubleshoot production issues, build IaC with Terraform/Azure DevOps, drive automation using AI tools, optimize performance and security, mentor junior engineers, participate in on-call rotation, and collaborate with cross-functional teams to deliver reliable cloud platform solutions.

Design, build, and operate secure, highly automated AWS cloud infrastructure using IaC and CI/CD. Troubleshoot production issues, improve observability and automation (including AI-assisted tools), collaborate with stakeholders, mentor junior staff, participate in on-call rotation, and drive platform innovation and governance.

Lead engineering teams to design and deliver enterprise IAM components across Azure, AWS, GCP, Office365 and SaaS. Own product backlog, prioritize user stories, coordinate with security and business stakeholders, define standardized cloud identity patterns, run POCs, and enable secure cloud migrations using IaC, CI/CD and DevSecOps tooling.

Deliver BI reporting, dashboards and analytical outputs to support business decisions. Validate and reconcile data, perform trend and impact analysis, collaborate with stakeholders, and contribute to automation and AI-enabled improvements.

As a Senior Machine Learning Engineer, you will design scalable AI systems, work with large datasets, write maintainable code, and continuously learn with a team of AI developers.

Design, build, deploy, and maintain the company's cloud platform infrastructure. Implement observability and alerting, design security policies, migrate projects to a centralized network, and maintain Infrastructure-as-Code repositories to ensure secure, highly available cloud workloads.