Top Python Jobs in Toronto

Design and implement features for content ingestion pipelines, metadata services, and content processing workflows. Build reliable, observable services that handle diverse file formats, malformed inputs, retries, and asynchronous workflows. Collaborate with ML, Search & Discovery, and Product to improve metadata extraction, classification, and discoverability while leveraging LLM-based tools and AI-driven practices.

Lead design and scaling of content ingestion, metadata, and processing systems. Build reliable pipelines handling messy formats, collaborate with ML, security, and product teams, mentor engineers, and drive platform and AI/LLM integration for content workflows and quality.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

Design, build, and optimize event-driven, distributed backend systems and APIs in Python for large-scale content metadata extraction and enrichment. Deploy and operate resilient AWS-based pipelines, collaborate with ML teams to integrate models, and ensure system performance, observability, and data integrity.

Build and optimize backend distributed systems to extract, enrich, and process metadata at scale. Integrate LLMs and ML models into production pipelines, ensure data quality and monitoring, collaborate with ML engineers and product, and maintain infrastructure, security, and performance of metadata systems.

Lead technical direction and architecture for the Fable backend, own platform reliability (databases, CI/CD, queues, observability), run incident response and postmortems, mentor engineers, collaborate cross-functionally with product/design/ML, and build platform capabilities enabling faster, safer delivery.

Design, build, and scale event-driven, distributed backend systems in Python to extract and enrich metadata at massive scale. Lead architecture, mentor engineers, integrate ML/LLM components, optimize performance, and ensure observability and reliability across AWS-based pipelines.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

Own and evolve product metrics, dashboards and performance reporting for Personal Banking. Build Power BI dashboards, design ETL pipelines and semantic models, analyze large datasets to generate insights, consult with partners, ensure governance and documentation, and drive improvements that support acquisition, retention and P&L management.

Lead and grow a software engineering team building enterprise contract-management AI products. Partner with product, ML, and design to deliver scalable SaaS solutions, own platform performance and operations, recruit and mentor engineers, and drive AI initiatives (LLMs, agents, RAG) with strong devops, testing, and observability practices.

Design, configure, and maintain Ignition-based SCADA systems for DER projects; perform AVEVA-to-Ignition migrations, develop HMI screens and dashboards, support commissioning, troubleshooting, upgrades, and QA; collaborate with engineering and operations, document standards, and participate in tiered on-call support to improve system resiliency and scale.

Lead instrument risk modelling for derivatives, hedge funds, liabilities and other securities. Build tools, validate models, collaborate with traders, finance and developers, and deliver risk reporting and advanced risk-management approaches.

Own operational excellence and governance for the PDE SDLC, manage and integrate core tools (ProductBoard, Jira, Amplitude, etc.), drive data integrity and automation, create SOPs and workflows, and partner with senior leadership to influence product, design, and engineering operations.

Provide functional and data-related client support for the d1g1t wealth management platform: configure the platform, onboard users, oversee reconciliations, support delivery (testing, documentation, training), build dashboards and knowledge base, partner with product teams, and identify product requirements to improve client experience and operational efficiency.

Design, build, and maintain scalable AI agents and enterprise-grade ELT/ETL pipelines using Python, PySpark, Databricks, and microservices. Deploy and integrate LLMs and agentic AI (Google ADK, Devin.AI, MCP), ensure data quality, security, and CI/CD-driven cloud-native operations, and drive data federation and Data Mesh solutions.

Design, build, and optimize scalable AI-enabled application features and services. Integrate ML models and generative AI, ensure secure and test-driven implementations, troubleshoot AI performance, mentor engineers, and drive AI architecture, CI/CD, and best practices across teams.

Design, implement, and maintain analytics data pipelines and warehouse models using SQL, Python, dbt, Airflow, and AWS. Support data governance, PII handling, CI/CD, and data quality. Collaborate with engineering, analytics, and business teams, participate in code reviews, and improve data engineering practices across the organization.

As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Develop and maintain scalable, reliable back-end systems and APIs, write maintainable code, and collaborate with global teams in a hybrid work environment.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

Design and develop user interfaces and backend architectures for web applications, ensuring seamless integration and optimal performance using modern web technologies.

The Software Engineering Lead will design and maintain enterprise software applications, develop APIs, and build back-end systems, ensuring quality and best practices.

As a Data Scientist at Sardine, you'll design and deploy data-driven solutions to prevent fraud, collaborating with clients and engineering teams to build and optimize machine learning models.

Join Hive to build and improve a marketing platform, focus on developing and shipping software, optimize system performance, and collaborate with teams.

As an AI Automations Engineer, you will design and build tooling for GTM, Operations, and Finance teams, automating processes and enhancing systems with a focus on AI.