Top Python Jobs in Toronto

Administer and integrate core SaaS and collaboration platforms, build and maintain NetSuite and other system integrations, automate workflows with scripting, manage user lifecycle and SSO, support macOS/Linux/Windows endpoints and hardware, triage and resolve support tickets end-to-end, maintain documentation and runbooks, and participate in on-call rotation.

Design and implement backend services and data pipelines to match business activities to context. Perform data-driven research, build scalable ML/LLM systems, write tests, create observability and runbooks, and collaborate across teams to deploy and operate production services.

Lead the Compass agent product as the technical and science owner: design and ship agentic/LLM-powered features end-to-end, set evaluation and experiment frameworks, build data-driven models and tooling, and collaborate across backend, data, and frontend to raise agent quality and enable safe action-taking behaviors.

Develop, test, and maintain marketplace applications using C#, .NET, Java, React and TypeScript. Write unit tests, participate in code reviews, debug production issues, support level-3 incidents, and collaborate with product and engineering teams. Continuously learn new tools (cloud, containers, AI) and follow engineering standards and automation best practices.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Integrate Workday/Notion, ensure security and data governance, design multi-model LLM reliability, and own production lifecycle including CI/CD, monitoring, and incident response while working with non-technical stakeholders.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Design, build, and deliver scalable Salesforce solutions across Sales Cloud, Data Cloud/Data360 and Agentforce. Lead data integration, identity resolution, data modelling, AI-assisted workflows, Flow/Apex development, LWC/Visualforce components, and platform administration. Own end-to-end delivery, governance, data quality, and release management while partnering with sales and data teams to implement automation and AI-enabled sales workflows.

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.

Lead design and implementation of production AI/LLM systems and scalable FastAPI backends, implement embedding-based semantic search with PostgreSQL/pgvector, build WebSocket real-time collaboration, own GCP/Kubernetes infrastructure (KEDA, Terraform), and mentor engineers while driving architectural decisions and feature rollouts.

Design and deploy closed-loop decision systems that estimate causal effects, quantify uncertainty, and optimize actions under constraints. Develop policy learning, bandits/decision-making, counterfactual evaluation, and production ML infrastructure to drive measurable business lift and safe automated decisions.

Design, develop, and maintain BI solutions and Power BI dashboards; extract, transform, validate and analyze data; produce reports, KPIs and scorecards; support data quality, reporting governance, stakeholder training, and automation to enable evidence-based decision-making and performance improvement.

Provide global 24x7 application and user support for banking operations technologies. Manage incidents to resolution, lead problem management, perform health checks, support changes and deployments, maintain monitoring and automation, run DR/testing and compliance activities, update knowledge articles, and collaborate with developers, business stakeholders and infrastructure teams.

Lead end-to-end analysis of customer transaction and engagement data to generate insights supporting customer relationship deepening. Perform data exploration, build analyses and visualizations using SQL, Python/PySpark and Power BI, and present findings to business partners to inform strategy and reporting.

Support model monitoring and governance for retail credit and scorecard models: develop and maintain monitoring code, perform performance and stability analyses, support validations and reviews, prepare executive reporting, coordinate remediation of findings, and assist platform migration and ML model monitoring initiatives.

Lead and support enterprise AI and analytics platform reliability, security, and scalability. Develop and maintain Amazon Bedrock capabilities and agentic workflows, apply DevOps and IaC practices, productionize services, run POCs and KTLO, and provide training and cross-team platform support.

Lead design, implement, operate, and maintain AWS infrastructure using Infrastructure-as-Code (Terraform). Build local tooling and deployment/observability integrations (e.g., CircleCI), participate in on-call rotation, improve CI/CD performance, mentor engineers, manage incidents, and move platform toward Kubernetes while supporting Ruby/Python/Bash production workloads.

Design, build, deploy, and operate AI-powered features and autonomous agents from prototype to production. Implement MCP servers, REST APIs, and integrations; collaborate with product, engineering, and QA; ensure secure data handling, observability, and high-quality releases.

Lead design, development, and optimization of Databricks/Spark-based ETL/ELT pipelines on Azure. Drive architecture decisions for lakehouse and data warehouse layers, improve performance, enforce data quality and governance, mentor junior engineers, and collaborate across stakeholders to deliver production-ready data solutions.

Build and integrate LLM-powered applications and agents (RAG, vector DBs, agent orchestration) into the PureFacts SaaS platform, design evaluation and safety frameworks, implement APIs and scalable services, and partner with product and engineering to deliver AI copilots and automation that improve revenue analytics and operational efficiency.

Lead design, development, and deployment of ML/AI solutions across the PureFacts platform. Own ML product vision and MLOps for multiple client models, build scalable data pipelines and cloud-based deployments, embed AI-driven automation and LLM features, ensure governance and explainability, and mentor a team of ML engineers to deliver measurable business impact.

Operate, maintain, and improve Lyft's logging and metrics infrastructure. Build tooling to automate platform operations, define SLOs, monitor metrics, support incident response and on-call rotations, collaborate with engineering teams on observability, document processes, and automate repetitive tasks to improve reliability and scalability of distributed systems.

Build and deploy production AI agents and MCP integrations for logistics workflows. Design multi-agent orchestration, implement LLM integration and retrieval pipelines (RAG/KAG/CAG), and deploy agent infrastructure on Kubernetes with observability and GitOps.

Design and implement enterprise cloud infrastructure across Azure and GCP. Architect Kubernetes platforms (AKS/GKE), CI/CD pipelines (Jenkins, Azure DevOps, GitHub Actions), GitOps and IaC (Terraform). Enforce Zero Trust and automate SOC 2/ISO 27001 compliance, build observability and developer self-service platforms, lead and mentor engineering teams, and drive cloud migrations, governance, and cost optimization.

Design, implement, and operate scalable AWS network architectures (multi-account, multi-region). Build IaC (Terraform, CDK, CloudFormation), integrate with CI/CD, troubleshoot DNS/routing/firewall/VPN issues, automate with Python/Bash/PowerShell, monitor network health, and partner with application, platform, and security teams to ensure secure, reliable cloud connectivity and compliance.

Ingest, process and transform large-scale behavioral data from web, mobile and connected TV into advertising data solutions. Build and maintain Big Data pipelines (BigQuery, Dataflow, Spark, Airflow), manage hybrid cloud infrastructure, generate audience segments and integrate with DMP/DSP systems, create visualizations, define data delivery architecture, and collaborate with data scientists, developers, and product teams to drive best practices and production-ready solutions.