Top Python Jobs in Toronto

Build and maintain scalable Python back-end microservices and data pipelines supporting document ingestion, RAG/IR systems, vector indexing, and LLM-powered knowledge services. Optimize performance, observability, APIs, and cloud deployments for production SaaS environments.

The Infrastructure DevOps Lead Analyst role involves designing and supporting CI/CD pipelines, automating processes, and ensuring application platform stability, scalability, and security. Responsibilities include troubleshooting, coding in Python, collaborating with teams, and guiding junior analysts.

Design, develop, test, and deploy scalable full-stack applications. Build Python backends, TypeScript frontends (Angular/React), REST APIs and microservices, integrate databases, use AWS for deployment, contribute to CI/CD, troubleshoot production issues, and follow best practices and testing processes.

Design, build, and operate ML infrastructure and scalable training/serving pipelines. Manage GPU clusters, cloud resources, CI/CD, IaC, SLOs, observability, security, and cost optimization. Collaborate with research teams to productionize models and create runbooks, templates, and tooling for repeatable ML workflows.

Lead development and evolution of AlphaSense's financial data taxonomy and generation rules. Serve as subject-matter expert, align cross-functional teams, define requirements for new datasets, ensure AI/LLM compatibility, maintain documentation, and build scalable frameworks to expand and maintain high-quality structured financial data for capital markets users.

Design and implement automated data quality frameworks and tests for BigQuery-based pipelines. Collaborate with data engineers and product to define data contracts and quality gates, build observability and anomaly detection, validate complex ELT/ETL processes, and integrate data tests into CI/CD workflows. Drive improvements in documentation, governance, and pipeline reliability.

Lead design and implementation of automated data quality frameworks and monitors for BigQuery pipelines. Collaborate early with data engineers and PMs to define data contracts, build tests in Python/SQL, integrate data tests into CI/CD, and improve data governance and observability for ETL/ELT workflows.

Serve as a client-facing solution architect for Coveo's AI relevance platform, translating business needs into scalable search, recommendation, and generative AI solutions. Lead solution design, architecture, implementation, stakeholder alignment, governance, agile delivery, and documentation across cloud and enterprise ecosystems.

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.

Join the ROOST Osprey team to design, implement, test, and maintain a high-performance, real-time safety rules engine. Work includes building data/event pipelines, scaling and containerizing services, integrating with Kafka and gRPC, reviewing community contributions, maintaining code quality and security, and engaging with users and open source contributors to prioritize and deliver features.

Lead and grow ROOST's engineering team, set technical direction, drive high-quality open-source development and release practices, engage the external community as stakeholders, and contribute hands-on to code, architecture, and operational excellence.

Build and operate Wealthsimple's ML platform to take models from experimentation to production: design architecture, build framework-agnostic training/inference services, ensure scalability and observability, enable self-serve tooling for data scientists, and own long-lived ML infra projects (Kubernetes, Ray, LLM integrations).

Build and operate platform infrastructure for AI development agents: isolated reproducible dev environments, agent orchestration and MCP server, secure integrations, OpenTelemetry-based observability, and CI/CD. Define standards, collaborate with Product and Platform teams, and drive adoption of agentic developer tooling across the company.

Lead architecture and core runtime foundations for a distributed scanner platform, driving reliability, observability, performance, and engineering rigor while collaborating with hardware and ML teams and mentoring engineers.

Research-focused internship for advanced PhD candidates to develop and evaluate neurosymbolic, knowledge representation, multimodal, and trustworthy AI methods. Duties include defining research questions, building prototypes and benchmarks, running rigorous experiments, collaborating with engineers and domain experts, and moving research toward production and publications.

Design, provision, and operate AWS cloud infrastructure and container platforms (ROSA). Build and maintain IaC (Terraform/Terraform Cloud), Ansible automation, CI/CD (GitOps/ArgoCD), observability (CloudWatch), security controls, and incident response while mentoring team members.

Design, build, and support high-performance front-office trading, analytics, and visualization tools for global equity. Implement real-time volatility analytics, pricing and hedging utilities, corporate actions adjustments, and integrate applications with containerized back-end infrastructure. Work directly with traders, quants, and cross-functional engineering teams to meet production performance, reliability, and scalability, and troubleshoot production issues.

Lead design and delivery of sales technology for equity derivatives, architect an OMS, integrate with vendor platforms and Bloomberg, design databases, ensure regulatory compliance, mentor engineers, and own production reliability and vendor relationships.

The Software Development Engineer in Test (SDET) ensures quality through automated testing for core platforms in the Utilities & Energy sector, collaborating closely with software engineers to embed quality practices, enhance CI/CD processes, and expand automated test coverage.

Lead and grow a remote AI engineering organization, manage ~30 forward-deployed engineers, maintain technical standards for agentic full‑stack applications, review architectures, run performance management and hiring, support client engagements, and drive team enablement, evaluation harnesses, and MLOps practices.

Provide strategic technical leadership to design and deliver secure, scalable cloud and hybrid application and infrastructure solutions. Define end-to-end architectures, lead cloud migrations, modernize infrastructure, design microservices and API integrations, ensure security/compliance, and produce architecture artifacts and governance documentation while collaborating with stakeholders and delivery teams.

The role involves architecting deep neural networks for object detection and tracking, optimizing performance, and developing data strategies in a low-data environment.

Develop and scale machine learning training systems for robotics data, optimize training pipelines, and collaborate with ML researchers to enhance autonomy models.

Build and productionize LLM-powered and agentic AI systems (RAG, fine-tuning, prompt engineering) and web app integrations. Lead design, set best practices, monitor and iterate on performance, mentor engineers, and partner with business and governance to deliver scalable, reusable AI solutions in cloud environments.

Lead detection and incident response across corporate security systems. Own investigations end-to-end, tune and automate detection (DLP, EDR, SIEM, phishing), produce runbooks and remediations, coach junior analysts, and partner cross-functionally to improve controls, compliance evidence, and AI-related security tooling.