Top Python Jobs in Toronto

Build and own end-to-end backend systems for forecasting and agentic LLM research: design, deploy, monitor, debug, and harden features and pipelines (benchmarks, agents, production services). Prototype quickly, work with LLM APIs and coding agents, and collaborate with researchers to improve evaluation and forecasting tools.

Design and deliver end-to-end marketing measurement and testing: data extraction, modeling, analysis, and visualization. Prioritize and route requests, translate business needs into technical requirements, mentor junior analysts, improve measurement standards, and drive actionable insights for retail and brand partners.

Design, build, and operate secure, scalable AWS cloud solutions. Develop and maintain reusable IaC (CloudFormation/CDK), policy-as-code (Rego), serverless and container patterns, observability, and automation. Collaborate with security/compliance, support PoC-to-production implementations, and enable development teams through guidance, standards, and mentoring.

Lead pre-sales and solution design for enterprise AI, cloud, software and data engagements. Craft proposals, proofs-of-concept, and reference architectures; run workshops; align stakeholders; support delivery transition; collaborate with Sales, Marketing and Delivery to grow the AI practice and drive measurable client outcomes.

Lead and execute perception R&D and engineering for factory robots: define multi-year roadmap, architect perception pipelines (detection, segmentation, 6-DoF pose, scene understanding), drive sensor fusion and calibration, establish data/model workflows and deployment practices, integrate with planning/control, hire and mentor engineers, and evaluate research for production adoption.

Design, develop, integrate, and maintain scalable web applications and backend services using .NET Core and Python. Build RESTful APIs and microservices, optimize database queries, implement security and authentication, support CI/CD and cloud deployments, participate in Agile/Scrum, perform testing and performance tuning, and produce technical documentation.

Operate and evolve an OpenStack-based private cloud and Ceph storage, troubleshoot full-stack issues, handle feature requests and support tickets, build automation and tooling, document runbooks, integrate storage with Kubernetes, and participate in on-call rotations to maintain platform reliability.

Operate and scale Kubernetes clusters across datacenters, triage infrastructure issues, build automation and tooling, collaborate with SRE/Networking/Security, support engineering teams, and participate in on-call rotation to maintain platform reliability.

Design, build, deploy, and operate an internal AI platform and AI-powered developer workflows. Implement CI/CD for models, prompts, and evaluations; integrate Bedrock/agent frameworks; ensure security, observability, reliability, and cost/version controls; respond to incidents and participate in on-call rotations; promote AI best practices and lead architecture discussions.

Design, build, and maintain integrations and automations between internal systems and third-party platforms (ERP/finance). Collaborate with stakeholders, use iPaaS tools and API standards, perform code reviews, participate in on-call rotation, and deliver well-tested integration projects on schedule.

Lead QA for investment and trade systems: design and maintain automated test frameworks, perform functional, performance and integration testing, lead and mentor QA team members, integrate testing into CI/CD/DevSecOps, analyze results, and drive testing process improvements to ensure production readiness and reliability.

Design and execute automated API and UI tests, develop and maintain automation scripts, verify data with SQL, report defects, and integrate test suites into CI/CD pipelines while collaborating with cross-functional teams.

Lead quality engineering for enterprise digital applications: design and maintain scalable automation frameworks, implement BDD and CI/CD quality gates, develop AI-assisted testing strategies, mentor QA engineers, collaborate with product and engineering teams, analyze results and ensure release readiness.

As an Android Engineer, you'll enhance automated tools for analyzing APKs, improve dynamic analyzers, and collaborate on security checks for Android applications.

The Manufacturing Bring-up Engineer oversees system testing and validation, collaborating with cross-functional teams to automate workflows and enhance manufacturing efficiencies.

Responsible for software integration and quality for Cerebras AI platform, focusing on automation, debugging, and cross-team collaboration. Develops QA strategies and testing methodologies to ensure product quality.

Design, build, and optimize cloud-native lakehouse data pipelines and curated data products using Python and SQL. Own data modeling, performance tuning, observability, governance, and reusable platform patterns to support analytics, embedded reporting, operational workflows, and AI/ML datasets. Mentor engineers, lead reviews, and improve platform reliability, cost efficiency, and data quality.

Own and build Snowflake-based data platform and ELT pipelines from SaaS sources into Snowflake, using Snowpark Python workloads. Design schemas, enforce governance, implement data quality and monitoring, optimize performance and cost, and enable AI-ready datasets (embeddings, feature stores, Cortex functions). Partner with RevOps, Finance, CS, and Analytics Engineers to deliver reliable, production-grade data for reporting and AI workflows.

Build and deploy generative ML features for Ideogram's app and API: fine-tune and ship models, design evaluations and datasets, run error analyses, monitor reliability, and collaborate with research, infra, product, and clients to meet quality, latency, and cost goals.

Provide operational support for a client through data collection, cleansing, modeling, reporting and visualization. Build automated standardized reports and surveys, interpret results, and make data-driven recommendations to support sales and marketing objectives.

Lead a Services AI Platform engineering team as a hands-on technical lead: write production-grade Python/Angular code, design multi-tenant agentic ecosystems, build fine-tuning pipelines and RAG retrieval systems, integrate vector and graph databases, enforce clean architectures (MCP servers), and work with SMEs to deploy agent-driven business use cases.

Lead and build shared AI infrastructure and production-grade agents for a product analytics team. Establish repo standards, evaluation tooling, governance compliance, monitor production agents, close data pipeline gaps, and drive adoption across ~35 analysts and 60+ products while interfacing with cross-team AI and data platform partners.

Design, train, and deploy real-time computer vision and multimodal perception models for person detection, tracking, pose/gesture/emotion recognition, and scene understanding. Build data collection/labeling pipelines, optimize models for edge inference, integrate perception with behavior and firmware, debug failures, and evolve perception architecture from prototype to deployed robot systems.

Act as the bridge between business and technical teams to gather, validate, and document requirements; advise stakeholders on system solutions; optimize workflows; translate BRDs into technical specs; oversee vendor/data integrations; design test cases and coordinate UAT and training; and support project scoping, timeline estimation, and risk management.

Lead design, migration, and optimization of enterprise CI/CD pipelines from IBM UrbanCode Deploy to Octopus Deploy across VM and mainframe environments. Refactor Groovy scripts into Octopus processes, develop C# deployment automation, validate production deployments, troubleshoot release issues, and drive CI/CD governance, security, and continuous improvement.