Top Python Jobs in Toronto

The Senior Site Reliability Engineer will automate operations using Python, manage Kubernetes and OpenStack clusters, and ensure high availability for enterprise infrastructures.

As a Senior+ Engineer, you will lead the design and architecture of impactful open-source projects while mentoring teams, coding, and engaging with the community.

The role involves designing and delivering open source software operations code in Python, ensuring system reliability, and collaborating with teams across the Linux stack.

As an Engineering Manager, you'll lead a remote team, manage MLOps projects, ensure high performance, and mentor engineers while driving product strategies.

As a Data Engineer, you'll develop storage infrastructure, collaborate with teams, implement data systems, and contribute to product strategy. You'll work with advanced AI systems and process production-grade data.

As a Senior Robotics Software Engineer, you will maintain and improve the Tally software stack, develop new software packages, enhance autonomy and navigation, and assist in DevOps and CI/CD processes.

As a Software Engineer, you will develop and maintain the Tally software stack, enhance robot navigation and autonomy, and support CI/CD processes.

Build and scale signal-driven GTM systems: design automation and AI workflows for account identification, enrichment, scoring, segmentation, and orchestration to drive outbound, ABM, and revenue generation. Partner with Marketing, Sales, and Success to run experiments, implement agent-assisted automation, and measure outcomes.

Design, build and maintain automated test suites and CI/CD test pipelines for cloud services. Collaborate with developers and product owners to assess risk, ensure quality, and drive improvements in stability, reliability and usability while participating in Scrum ceremonies and mentoring team members.

Develop and enhance GIS capabilities within a core C++ OMS application, configure map services, design cartography, integrate spatial databases, resolve spatial data issues, and collaborate with product teams using Jira and GitHub.

Provide expert MongoDB technical support, troubleshoot complex deployments, administer Linux systems, advise on schema/design, performance tuning, backups, monitoring, and collaborate on support content and product strategy while occasionally traveling for team or customer meetings.

Own day-to-day GTM revenue systems and build AI-native automations. Responsibilities include Salesforce administration, n8n workflow development, deal desk and commissions support, tooling administration, integration monitoring, and converting repetitive tasks into automated, agentic workflows using Claude Code and other AI tools.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Maintain and evolve the Financial Data content set: ensure data quality, produce weekly schedules, prioritize research via data-driven reporting, perform gap analyses, audit vendor/entity data, manage lifecycle events, optimize workflows and SOPs, synthesize KPIs and client metrics, and administer user access for proprietary research tools.

Build and operate AI-native backend services that orchestrate multi-step, stateful LLM agents for GTM workflows. Design model-agnostic abstractions, integrate external systems, deploy containerized services on Kubernetes, ensure observability, and run experiments to measure effectiveness.

Lead architecture and evolution of scalable, fault-tolerant backend systems powering real-time AI agents and conversation intelligence. Design data storage and retrieval, partner with ML engineers to operationalize AI, drive production performance, cost efficiency, observability, and mentor senior engineers while owning production reliability.

Build and own agentic systems on ClickStack to investigate incidents: design reusable skills, implement MCP servers and SDKs, integrate telemetry, optimize latency/cost, run evals, and collaborate with OSS contributors and customers to make agents production-ready and observable.

Lead development of a production autonomous-agent intelligence layer: implement closed-loop learning from trade outcomes, build evaluation and automated strategy generation, monitor fleet performance, manage risk and capital allocation, own model training/inference pipelines (including LLM hosting/fine-tuning), and build telemetry and data capture for real-time market adaptation.

Lead mixed-mode design verification methodology for complex SoCs. Define co-simulation EDA environment, create Verilog/SystemVerilog testbenches and behavioral models, support analog/digital teams, and perform AMS simulations for block- and chip-level verification.

Senior Business Systems Analyst supporting finance projects: gather requirements, define system scope, assess risks, recommend IT enhancements, provide sophisticated analyses, serve as SME for stakeholders, and ensure compliance while guiding cross-functional teams on Trade Capture, Risk Management, FX/Rates products.

Lead AI-enabled financial planning and strategic analysis, build data-backed business cases, refine unit economics, deliver executive-level insights, integrate ML into FP&A, and track KPIs to drive profitability and strategic decisions.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.