Top Python Jobs in Toronto

Join a cross-functional team to develop data products, write efficient code, influence AI roadmap, and collaborate with diverse professionals.

The role involves designing and developing front-end and back-end components, mentoring team members, ensuring system efficiency, and optimizing platform performance in a collaborative team environment.

The Senior Staff Software Engineer will architect Affirm's lakehouse analytics platform, implement data governance, mentor engineers, and collaborate cross-functionally to optimize data infrastructure and solutions.

Lead and optimize enterprise Oracle databases for performance, availability, and security. Own backup/recovery and high‑availability strategies, troubleshoot complex incidents, collaborate with SRE/cloud/application teams, enable on‑prem to cloud migrations, and drive automation and monitoring improvements.

Lead and scale Databricks-based AI/ML professional services: design and deploy enterprise agentic solutions, establish Lakehouse foundations, implement MLOps/LLMOps and CI/CD, integrate LLM providers, run workshops and POCs, and ensure governance, observability, and measurable agent performance.

Embed with clients to design, prototype, and productionize GenAI and agentic solutions on the Databricks Intelligence Platform. Build RAG and multi-agent systems, develop Lakehouse data/feature foundations, operate MLOps/LLMOps pipelines, and ensure observability, governance, and secure deployment across cloud platforms.

Provide advanced analytics, reporting and ML-based insights for collections operations. Conduct deep data analysis, build and deploy predictive models, standardize reporting, recommend process and technology improvements, and support senior management with strategic, data-driven recommendations.

Design, build, and deploy production AI agents and MCP servers to automate logistics workflows. Implement LLM integrations, multi-agent orchestration, and hybrid retrieval pipelines (RAG/KAG/CAG). Deploy agent infrastructure on Kubernetes with GitOps and observability, and collaborate with cross-functional teams to translate operational requirements into agent capabilities.

Build and operate end-to-end time-series forecasting and MLOps systems for battery energy storage: data pipelines, feature engineering, model training (including Temporal Fusion Transformer), deployment, monitoring, and production support. Create Airflow DAGs, ensure pipeline reliability, collaborate cross-functionally, and produce dashboards and documentation for stakeholders.

Embed with clients to design, build, and deploy Snowflake-native data pipelines and agentic AI solutions. Implement Cortex AI, optimize queries and cost, run MLOps/LLMOps with CI/CD and IaC, and deliver workshops, PoCs, and production-grade agent orchestration across enterprise domains.

Lead enablement of Canada P&BB data, analytics, and AI capabilities. Partner with product owners and business teams to define, prioritize, and deliver production data and AI use cases. Translate business problems into implementation-ready solutions, guide platform and cloud (AWS/Azure) implementations, align initiatives to target architecture and roadmaps, and collaborate across business, data, and engineering teams to ensure scalable, auditable production outcomes.

Lead campaign analytics by monitoring KPIs, diagnosing performance issues, delivering strategic insights, segmenting member data, operationalizing reporting, and partnering with stakeholders to improve marketing campaigns and identify growth opportunities.

Lead a team to design, build, and deploy scalable AI agents for HR and Finance. Define tooling and framework choices, integrate LLMs and orchestration, implement RAG and vector search, ensure compliance, monitoring, and continuous learning, and collaborate across product, engineering, and vendors to deliver enterprise-grade AI automation.

Support and enhance Aviva Canada's Economic Capital internal model via stochastic simulation, quantitative risk analysis, and model validation. Build and maintain statistical and catastrophe modelling frameworks, analyze complex actuarial datasets, inform capital metrics and allocation, and communicate technical findings to stakeholders. Contribute to climate risk (OSFI B-15) physical risk modelling and model governance.

The Senior Manager, Cloud Development leads strategic planning and hands-on development of cloud-based solutions using GCP, focusing on AI/ML integration and system reliability.

Develop and maintain InstaLILY's AI agents platform, collaborate with product/design/growth and enterprise stakeholders, ship customer-facing features, ensure reliability via testing and CI/CD, and iterate on feedback while staying current on AI trends.

Develop, validate, and productionize machine-learning and statistical solutions for supply-chain use cases (demand forecasting, inventory optimization). Build model-ready datasets, implement models in Python/SQL, perform experimentation and time-aware validation, and operationalize outputs with reliable pipelines, monitoring, and governance. Collaborate with stakeholders to ensure solutions are measurable, explainable, and aligned with operational constraints.

Design, implement, and automate test procedures, fixtures, and software for PCBs, subassemblies, and systems. Develop test plans, execute prototype bring-up and debug, perform root-cause analysis, provide DFT feedback, and collaborate with electrical, firmware, mechanical, and manufacturing teams to ensure product reliability and testability.

Lead design and delivery of the integrations platform supporting 250+ integrations. Build APIs, integrations, tooling, and observability; engage customers; optimize performance and scalability; set architectural direction and best practices.

Design, build, and optimize scalable streaming and batch data pipelines and storage for petabyte-scale IoT/time-series data. Implement real-time ingestion, observability, data lineage, and write maintainable Java/Python platform code while shaping architecture for reliability and scalability.

Build and maintain scalable pricing data pipelines and schemas, implement data quality systems, develop ETL/self-service tools, tune SQL/MapReduce jobs, write tested maintainable code, perform code reviews, and collaborate with engineering, data science, and business partners.

Lead design and implementation of a scalable data platform handling petabytes of telematics and time-series data. Build and optimize streaming and batch pipelines, design storage solutions, implement observability and data lineage, manage HA infrastructure, and write maintainable Java/Python code while shaping long-term architectural strategy.

Entry-level digital consulting role supporting implementation of technology solutions: assessments, process mapping, data conversions, user testing, stakeholder interviews, project logistics, presenting findings, and client support across industries.

The role involves building the data platform, overseeing architecture, improving performance and reliability, and collaborating with various teams. Responsibilities also include technical direction and mentoring other engineers.

Lead advanced analytics for Canadian Personal Banking products: build scalable data assets, perform regression/clustering/time-series/causal analyses, develop production-ready models and automations, mentor junior scientists, partner with business and tech teams, and communicate insights to influence product, pricing, and portfolio decisions.