Top Python Jobs in Toronto

Build, ship, and maintain full-stack features for North: large-scale file uploads, shared knowledge bases, agent extensibility, and data-sync pipelines. Own end-to-end design, implementation, testing, launch, and iteration while working closely with Product, UX, and modeling teams. Prioritize security, performance, and mentoring.

Build and lead mapping-focused features using Lyft's in-house navigation system. Design, implement, test, and operate scalable, fault-tolerant distributed systems in cloud environments. Collaborate with product, data science, analytics, operations, and AI/ML teams; own features from design through launch; participate in code reviews and on-call rotations; share knowledge via talks and mentoring.

Participate in application development and maintenance, design and implement programming solutions, write and optimize SQL and scripts, troubleshoot data pipelines and jobs, consult with users, coach junior staff, assess security risks, and ensure compliance. Support ETL, job scheduling, data validation and recon processes in a financial services environment.

Translate business priorities into practical technology, data, automation, and AI-enabled opportunities. Develop customer roadmaps, gather and synthesize requirements, run discovery and POCs, advise on AI use cases and adoption, and drive stakeholder alignment across business and D&T teams to improve investment-related workflows.

Design, architect, and deploy production-grade generative and agentic AI systems (RAG, knowledge graphs, multi-agent orchestration). Build context engineering, retrieval, observability, and governance for scalable, grounded AI solutions; integrate with cloud infrastructure, container platforms, and CI/CD; mentor engineers and ensure responsible AI practices.

Lead applied research on evaluation, monitoring, and safety for generative AI (LLMs, VLMs, agentic systems). Develop scientifically grounded metrics, AI sandbox environments, benchmark methodologies, and safety guardrails. Partner with engineering and product teams to deploy evaluation, monitoring, and governance practices into production, and translate Responsible AI requirements into measurable, scalable solutions.

Build and maintain platform and infrastructure for quantum hardware development: design APIs and internal tooling, manage AWS-backed data infrastructure, implement CI/CD and containerized deployments, collaborate with hardware researchers, and perform DevOps tasks across multiple repositories.

Manage, support, and modernize high-volume relational database infrastructure. Lead SQL Server issue resolution, performance tuning, HA/DR design, backup validation, automation (DB-as-Code), monitoring, and Level 2 operational/project support, including 24/7 on-call participation and cross-team vendor collaboration.

Lead advanced analytics and ML initiatives to build predictive models, drive AI governance, architect scalable data strategies and lakehouse solutions, enable rapid prototyping and MLOps, embed with business teams to discover problems and deliver measurable business impact across cross-functional stakeholders.

The Senior Guidewire PolicyCenter Developer designs, configures, and develops PolicyCenter solutions, ensuring best practices in coding and deployment while mentoring the team.

As a Full-Stack Software Engineer, you will build end-to-end features, develop backend services, and create frontend applications while using AI-assisted tools and collaborating with teams.

Own and triage the support queue for Limina customers: reproduce and resolve API, file processing, redaction, and container deployment issues; distinguish product vs. customer environment issues; escalate defects with reproduction steps; maintain runbooks and improve support processes; communicate status to technical and commercial stakeholders.

Develop and optimize software components and pipelines for Roche's SBX sequencing platform. Implement and maintain production-ready code (Python, C/C++), CI/CD, containerized ML deployments, and efficient genomic data processing using HPC or cloud resources (SLURM, AWS/GCP). Collaborate on algorithm implementation, performance profiling, and workflow engineering (Nextflow/Airflow).

Lead end-to-end analytics and AI/ML initiatives: diagnose performance across customer/product/advisor dimensions, build scalable datasets, dashboards, segmentation, and predictive models, implement cloud-based ML solutions, produce executive-ready visualizations and stories, mentor teams, and drive adoption of advanced analytics across business stakeholders.

Design, build, test, and maintain Magnet One cloud platform features across front-end and back-end. Collaborate on architecture, perform code reviews, troubleshoot defects, and drive best practices for secure, performant, and maintainable software.

Lead DevOps and SRE initiatives for cloud data platforms: build and manage Kubernetes deployments (EKS/GKE), IaC with Terraform, CI/CD (Jenkins/ArgoCD/GitHub Actions), cloud data services, observability, automation with Python, cost optimization, and drive company-wide best practices for reliability and scalability.

Partner with sales and customer technical teams to design and deliver scalable Databricks Lakehouse architectures, present reference architectures and demos, validate integrations, and drive adoption of data engineering, data science, and ML solutions. Serve as a trusted technical advisor throughout the sales lifecycle and manage delivery of production data solutions.

Lead and grow an ML infrastructure team to build, scale, and operate training, deployment, and agent/LLM systems across on-prem and cloud HPC. Partner with research, ML engineering, and platform teams to translate requirements into robust MLOps solutions, optimize GPU utilization, and establish company-wide ML infrastructure standards while mentoring engineers and driving model-driven practices.

Design and operate a scalable ML platform for model training, deployment, and governance while optimizing performance and supporting data scientists in productionizing workflows.

Own Faire's ML platform architecture and standards across training, inference, feature management, governance, and MLOps. Architect scalable Databricks/Spark/Delta Lake/MLflow workflows, optimize cost and reliability, integrate LLM workflows, mentor engineers, and lead cross-functional initiatives to increase data science velocity.

Lead design and delivery of enterprise-scale Azure data platforms and AI-powered automation. Build reusable frameworks, solve complex technical problems, mentor engineers, influence stakeholders, and champion data engineering best practices.

Design, automate, and operate hybrid cloud and datacenter networks. Develop and maintain Ansible playbooks and automation, troubleshoot connectivity, implement security, monitor systems, and participate in 24/7 on-call escalation for highly available network architectures.

Work embedded with strategic customers to design and build end-to-end integrations and solutions using ElevenLabs technologies. Own major projects, collaborate with customer engineers and executives, and solve complex technical problems with strong coding, architecture, and communication skills.

Build and evolve scalable observability systems: high-throughput telemetry ingest, cost-efficient storage, instrumentation libraries, SLO/alerting infrastructure, and integrations to improve reliability, diagnostics, and incident response across multi-cloud infrastructure.

Develop and deploy data-driven strategies and machine learning models for Life & Health insurance using SQL and Python. Analyze data, automate processes, present insights to technical and non-technical stakeholders, and explore AI opportunities to improve decision-making.