Top Python Jobs in Toronto

Build and automate cloud cost optimization and governance across AWS and GCP. Analyze billing and usage, identify and implement savings, maintain cost attribution, create reporting and anomaly detection, and write Python, Terraform, and Lambda automation. Partner with Engineering, Finance, Product, and leadership on architecture tradeoffs, forecasting, budgeting, and cost accountability.

Design and implement end-to-end AI systems and production-grade components. Build and maintain RAG pipelines, backend services, APIs, and data pipelines. Write Python code using Azure AI SDK. Implement document digitization, vectorization, semantic search, and content summarization. Prepare training/evaluation datasets, participate in code reviews, and drive engineering quality and deployment of AI-powered SaaS workflows.

Owns reliability, scalability, observability, and incident response for a mission-critical SaaS platform. Responsibilities include 24x7 on-call support, root cause analysis, automation, AWS infrastructure design, EKS/Kubernetes and Docker operations, Terraform and Helm deployments, CI/CD maintenance, cloud networking, monitoring with Datadog, Grafana, and Prometheus, database operations, and migration toward Kubernetes. The role also develops self-healing systems, documentation, runbooks, and cross-functional customer-focused reliability practices.

Train and operate production machine learning models on Amazon SageMaker using AWS Trainium. Optimize PyTorch code for Trainium compilation, memory, throughput, and distributed training; diagnose hardware, compiler, device, data, and code issues; build cost-aware end-to-end training pipelines; and collaborate directly with client and internal engineering teams to deliver production workloads.

Design insurance pricing, reserving, forecasting, and portfolio-risk scenarios for AI model training. Create actuarial analyses and reference answers, evaluate AI outputs for quantitative and methodological accuracy, identify unsupported assumptions and errors, and provide structured feedback. The role requires explaining actuarial methods and business implications to technical and nontechnical audiences while participating in calibration and onboarding sessions.

Design and own scalable data pipelines, warehousing architecture, and dbt transformation layers from ingestion through analytics and incentive payouts. Lead data-platform architecture, including OLake, Iceberg, and Snowflake workflows. Establish performance, cost, quality, lineage, observability, and schema-drift standards. Partner with engineering and analysts on telematics accuracy and driver incentive reporting while using AI coding tools to accelerate development.

Design UI and UX flows, improve design systems, maintain design integrity through product launches, and use data, user research, and A/B testing to refine experiences. Collaborate with engineering, product, marketing, sales, and customer success teams to create accessible, high-quality product designs and continuously iterate based on stakeholder and user feedback.

Manage security operations, incident response, detection engineering, vulnerability and patch oversight, SaaS and endpoint security, cybersecurity risk assessments, and AI security strategy. Lead and develop security engineers, serve as senior Incident Commander, tune CrowdStrike detections, build coverage across endpoint, SaaS, identity, cloud, and proxy signals, and communicate security risks to technical and executive stakeholders.

Develop and execute growth marketing for enterprise AI systems by translating technical work into compelling stories, videos, demos, and case studies. Shape company positioning, own social and content channels, build marketing tools and agentic workflows, track pipeline impact, and run experiments. The role combines strategic marketing, business storytelling, content creation, coding, and AI research.

Develops and communicates the business impact of enterprise AI systems through writing, video, demos, case studies, and digital channels. Shapes company positioning, owns LinkedIn, YouTube, X, and future channels, and runs performance-driven marketing experiments. Builds marketing tools such as scrapers, agentic case-study generators, microsites, and pipeline dashboards using Python or TypeScript. Requires strong writing, business understanding, hands-on coding, AI knowledge, and a portfolio of shipped work.

Lead and deliver your team's quarterly goals, guide engineers through ambiguous problems, and collaborate with product, design, and analytics. Identify and resolve technical, process, and operational issues, support availability and on-call, enforce code and design quality, and mentor team members to grow talent and maintain high engineering standards.

Sets technical direction for Helix’s Experience platform and broader engineering organization. Designs and implements distributed, full-stack genomic software systems focused on patient and provider experiences. Leads complex projects from scoping through production, advances AI-assisted engineering practices, establishes quality and delivery standards, mentors technical leaders, and drives cross-functional collaboration with product, design, research, scientific, and business teams.

Design, build, and operate reliable backend services, APIs, data systems, and asynchronous workflows. Own technical projects from architecture through production, process high volumes of events and customer data, and improve platform reliability, performance, observability, interoperability, and scalability. Collaborate with founders, product engineers, applied AI, and customer-facing teams while shaping engineering standards and the company’s technical foundation.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

Develop and automate Python-based test cases on the PACE platform for CMIS optical modules, execute and analyze tests, log and triage firmware bugs, perform manual lab testing and hardware setup, collaborate with developers, mentor junior engineers, and drive continuous test process improvements.

Leads complex technical implementations and workflow optimization for medical imaging information systems. Designs, configures, validates, and deploys HL7/DICOM integrations; troubleshoots application, infrastructure, and operational issues; supports client and third-party coordination; delivers user and administrator training; documents implementation artifacts; and contributes to reporting, SQL queries, migrations, anomaly investigation, and workflow improvements. The role requires healthcare IT integration experience, Linux and SQL proficiency, radiology workflow knowledge, and up to 10% client travel.

Leads complex healthcare IT implementations for medical imaging and image exchange solutions. Designs, configures, validates, and deploys HL7/DICOM integrations; diagnoses application, workflow, and infrastructure issues; supports go-lives; trains users and administrators; coordinates milestones with project teams, clients, and vendors; documents configurations and architecture changes; and contributes to custom queries, reports, migrations, and process improvements.

Lead enterprise data architecture across AWS and Databricks Lakehouse: design scalable data models, ensure governance and data quality, mentor teams, evaluate and adopt data technologies (Delta Lake, Unity Catalog), oversee large projects and deliverables, and incorporate generative AI to accelerate data modeling and delivery.

Build and maintain Mural’s integration platform, including two-way sync with systems such as Jira, Linear, and Asana; Microsoft, conferencing, notification, and content integrations; public APIs; file handling; and format conversion. Lead projects end to end, design scalable and secure systems, manage third-party API concerns, create testing and documentation strategies, mentor engineers, and collaborate across teams on technical boundaries and platform capabilities.

Leads DFT architecture and implementation for chiplets, PHYs, controllers, and 2.5D/3DIC systems. Develops scan, BIST, ATPG, hierarchical test access, die-to-die test, KGD screening, and customer-ready DFT collateral. Partners with digital, analog, physical design, verification, STA, and test engineering teams. Owns DFT tools, constraints, coverage, simulation, post-silicon debug, ATE pattern bring-up, and methodology development.

Manage discretionary managed-account portfolios, model portfolios, investment mandates, trading oversight, performance reporting, compliance monitoring, and client reporting. Partner with advisors, sub-advisors, technology, operations, compliance, and product teams to improve managed-account platforms and workflows. Support investment strategy, market communications, portfolio accounting, fee billing, attribution analysis, and operational automation using data analytics and technology tools.

Develop and maintain secure, highly available public and private cloud infrastructure using automation, Infrastructure as Code, Kubernetes, Puppet, and deployment pipelines. Responsibilities include cloud architecture, IAM, networking, scripting, containerization, monitoring, troubleshooting, disaster recovery, security collaboration, and on-call support. The role also mentors engineers, researches emerging technologies, and partners with DevOps and Basis teams to support cloud-based applications.

Lead full‑stack development and reliability improvements for the Firefox Add‑ons ecosystem, building features, automated moderation pipelines, reviewer tools, and observability. Improve platform reliability, respond to incidents, and help keep the ecosystem safe and trustworthy. Collaborate across design, product, QA, and community contributors and contribute openly through PRs and code reviews.

Leads end-to-end machine learning delivery for insurance transformation programs, including feature engineering, model development, validation, tuning, explainability, governance, and production readiness. Translates business objectives into analytics use cases and delivery plans, manages client relationships, coordinates distributed teams, and supports architecture, UAT, MLOps, monitoring, and deployment discussions. Requires strong Python, statistical modeling, SQL, large-scale data, stakeholder communication, and production ML experience.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.