Top Python Jobs in Toronto

Lead design, delivery, and support of SmartStream TLM reconciliation solutions. Oversee full reconciliation lifecycle, validate TLM configurations, review Oracle PL/SQL and Unix scripts, guide match-rule design, manage a team of analysts/developers, ensure testing, controls, CI/CD and stakeholder collaboration, and resolve complex production issues.

Build an internal AI developer platform that provisions agent configs, context tooling, eval harnesses, and rollout mechanics to make AI-assisted engineering the default. Integrate LLMs and third-party coding tools, enable self-serve agent creation, embed safety and audit controls, and measure impact on developer productivity.

Lead market entry in Canada: hire and manage a regional technical team, engage C-suite customers, identify use cases, design bespoke integrations, build demos and prototypes, drive initial customer wins, and collaborate with product and research to scale ElevenLabs' voice and audio AI solutions.

Design and build scalable .NET backend services and gRPC microservices; optimize data layers (SQL Server, MongoDB, Elasticsearch); implement cloud integrations on AWS and Azure; ship AI/ML features (LLMs, RAG, vector search, ranking, personalization) and operate MCP servers; contribute to CI/CD, observability, containerization, and production ML integrations impacting high-traffic consumer marketplaces.

The Data Operations Specialist will manage data operations, ensuring quality data services and compliance, while optimizing data workflows and conducting audits.

Design, develop, and deploy specialized AI agents and tool integrations to automate design and development workflows. Build interfaces (MCP/Function Calling), perform prompt engineering, enforce accessibility and quality standards, collaborate with product/design/engineering, and extract metrics from agentic workflows to improve UX and compliance.

Design and develop Generative AI platforms, ensuring scalability and integration of AI capabilities, while leading innovative projects and mentoring developers.

As a Principal Software Engineer, you will design distributed systems, enhance product automation, and develop software solutions within a collaborative global team to address complex business challenges in finance.

The GenAI Platform Engineer will design, develop, and maintain Generative AI platforms using Python, Node.js, and TypeScript, ensuring scalability and integration across Citi's business units while mentoring junior members and collaborating on innovative projects.

Lead applications systems analysis and programming activities, partnering with management to implement system enhancements and ensure compliance with industry standards. Provide expertise in applications programming and serve as an advisor to mid-level developers, while managing high-impact projects and delivering innovative solutions.

Join Citi's Prime Analytics team as a Python Developer, collaborating with market quants to enhance analytics offerings using Python and related technologies, ensuring integration and compliance while delivering innovative solutions.

The Senior Pyspark Data Engineer will manage large-scale data processing systems, conduct feasibility studies, provide user support, and develop security measures while acting as a subject matter expert in applications development.

The role involves leading application systems analysis and programming, managing a team, and working closely with data engineers to deliver high-quality products on the Data Technology team.

This role involves designing and developing scalable data solutions, automating tasks, and collaborating with teams to deliver business value. Responsibilities include strong database development using SQL and Python, as well as leading innovative projects and ensuring compliance.

Own model selection, evaluation, and deployment for voice and multimodal restaurant AI agents. Build datasets, experiments, and monitoring to improve accuracy, safety, latency, and cost. Partner with product and engineering to ship model changes with guardrails, rollouts, and clear metrics tied to business outcomes.

As a Senior Software Engineer, you will develop AI-powered systems to enhance accounting workflows, build backend services, and ensure optimal performance and reliability of AI solutions.

Build and operate scalable, secure cloud infrastructure for real-time AI services. Improve reliability with SLOs, observability, capacity planning, and failure testing. Implement IaC, CI/CD, deployment safety, secrets and vulnerability management. Partner with product and AI teams, diagnose distributed failures, reduce cost, and automate platform patterns to enable faster, safer releases.

Build end-to-end AI-driven restaurant products from discovery through production. Own full-stack interfaces, backend services, agent workflows, integrations (POS, reservations, telephony), evaluation systems, and instrumentation. Design agent workflows combining models, rules, memory, and human escalation. Work directly with product, design, and restaurant users to iterate, improve reliability, latency, accuracy, and cost, and ship production-quality solutions.

Join the SOTA Training Platform team to bring up ML models on Cerebras systems, enhancing performance across the software stack and debugging issues.

The role involves designing AI models and training methods leveraging wafer-scale hardware and collaborating with various teams and partners in AI and computational science.

Design and architect front-end network fabrics for AI/ML and HPC clusters. Automate deployment of network infrastructure, debug networking issues, and lead technical projects across teams.

Design, implement, optimize, and validate high-performance ML and linear algebra kernels for Cerebras hardware. Develop low-level assembly and CSL routines, measure and tune performance, build testing methodologies, and collaborate with chip and system architects to maximize compute utilization for AI and HPC workloads.

Join the Inference Core Model Bringup team to bring up ML models on Cerebras CSX systems, focusing on performance, optimization, and debugging.

Join the Kernel Reliability team to improve the reliability of compute clusters, assist in debugging, and enhance system tools alongside hardware teams.

The R&D Engineer will design and implement performance workloads on Cerebras' wafer-scale hardware, collaborating across teams to advance AI and scientific computing. Responsibilities include optimizing performance models, contributing to the technology roadmap, and publishing research findings.