Top Python Jobs in Toronto

Develop platform driver code, perform diagnostics testing, collaborate with hardware engineers, and support system deployment in AI datacenters.

Lead the Treasury team to develop and maintain backend services ensuring reliability in financial transactions, while implementing features and improving systems.

As the AI Agents Solutions Architect at Kraken, you'll design and build an AI-native finance operating system, automating finance operations and ensuring compliance in a regulated environment.

The Junior Mobile Core Support Engineer will troubleshoot and resolve telecom network issues, support customer queries, and maintain network performance while collaborating with engineering teams.

As a Backend Software Developer, you'll design and implement microservices, model data in MongoDB, and ensure systems' reliability and performance. You'll work with AI capabilities and partner integrations, emphasizing CI/CD and observability.

As a Senior Full-Stack Engineer, lead development of secure AI-driven SaaS applications, focusing on backend architecture, frontend design, and AI integrations. Collaborate with teams to operationalize models and enhance application functionality through cutting-edge AI solutions.

The role involves leading technical teams to develop high-quality software, mentoring engineers, and contributing to open source projects. Responsibilities include planning, execution, and enhancing the Linux user experience across various teams and technologies.

Design and architect scalable backend services, develop robust governance systems, build SaaS solutions, and mentor junior engineers to enhance IoT platform capabilities.

The Director of Pricing develops pricing strategies and enhances pricing models for workers compensation, collaborating across departments and analyzing data to support decisions.

Lead and mentor engineering teams to deliver high-quality, AI-driven software, ensuring accountability and continuous improvement in development processes.

As a Software Developer in Business Intelligence, you'll analyze complex datasets, collaborate with stakeholders, build data models, and develop insights for product optimization in the interior decor industry.

Research AI threats, analyze threat actor behavior, produce actionable intelligence, collaborate with teams, and publish findings in the cybersecurity domain.

Partner with web product, marketing, engineering, and finance teams to own web analytics strategy and drive actionable decisions. Design and evaluate experiments and causal analyses, define metrics, ensure event instrumentation, and translate findings into product and growth recommendations. Use SQL and Python/R for statistical analysis, deploy experimentation platforms, and help shape AI-native analytics automation. Focus is on measurement, causality, and moving teams to act—not production ML.

Embed with product, engineering, marketing, and finance teams to own analytical narratives, establish causality via experimentation and causal inference, define measurement strategies, drive actionable insights, shape AI-native analytics tooling, and mentor senior analysts. Focus is on decisions and measurable impact rather than dashboards.

Partner with product, engineering, marketing, and finance to drive decisions using causal inference, experimentation, and analytics. Own measurement strategy, present actionable insights, shape AI-native analytics tooling, and ensure analyses translate into product and business outcomes.

Design, build, and deploy LLM-powered AI systems including RAG pipelines, vector/graph retrieval, and multi-agent orchestration. Solve information extraction, storage, and retrieval problems for structured and unstructured data, implement evaluation and MLOps practices, and collaborate cross-functionally to productionize scalable, secure AI services.

Design, build, and operate AWS-native AI/ML systems: RAG pipelines, fine-tuned LLMs, and microservices. Implement IaC (Terraform), CI/CD, policy guardrails, and production deployments integrating Bedrock/SageMaker, LangChain, and telemetry for secure, scalable enterprise use.

Join an ASIC design team to develop and implement ASIC flows, automate design processes with scripting, perform synthesis and rule checking, and support design tasks for coherent optics chips while learning and collaborating with engineers.

Research, evaluate, and manage partnerships with data providers. Assess datasets for coverage and interoperability, support deal structuring, maintain partnership pipeline, and collaborate with product, engineering, and legal to integrate data into the platform.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Founding full-stack engineer to architect and build a production platform combining large-scale data systems and AI-native agents. Work across backend, frontend, data pipelines, agents, and deployment systems; define architecture, devops, CI/CD, and early engineering processes while shipping features and growing the team.

Lead a hands-on engineering team building shared backend and cloud platform services. Architect, code, and ship high-volume REST APIs and reusable libraries, drive secure SDLC and CI/CD, mentor engineers, and promote AI-augmented delivery practices.

Lead architecture and delivery of production-grade applied AI systems (LLMs, retrieval, agents). Solve cross-stack technical problems, define platform direction on AWS, optimize data and database design, improve quality/QA and observability, mentor a ~20-person team, and translate ambiguous product opportunities into measurable technical outcomes.

Lead and build a new GTM Engineering function within Revenue Technology. Hire and mentor engineers, remain hands-on coding, and deliver durable automation and AI solutions across sales, marketing, and customer success. Partner with revenue leaders and platform teams to translate strategy into measurable engineering outcomes, establish standards for discovery, build quality, and safe AI deployment, and iterate rapidly to ship practical revenue workflow improvements.