Top Python Jobs in Toronto

Lead Internal Audit Analytics & AI strategy; integrate analytics and AI across the audit lifecycle; develop scalable analytics, automation and responsible AI solutions; manage and mentor an analytics team; provide technical direction for Power BI, Alteryx, SQL, Python and LLM tools; partner with audit and business stakeholders and present insights to senior management.

Drive applied AI for R&D by analyzing structured and unstructured data, developing representation, reinforcement, and generative learning methods, implementing ML and optimization algorithms, building reproducible workflows and toolkits, and collaborating with scientists and engineers to improve modeling, simulation, and research efficiency for quantum-computing R&D.

Design, optimize, test, and benchmark prompts and agent tool descriptions to improve AI performance. Collaborate with Customer Success and Data Science, automate evaluation and testing, document prompt practices, and research new NLP and model-specific prompting strategies.

Own, harden, and operate bare-metal virtualization, HA storage, Linux pools, networking, monitoring, and incident response. Automate configuration and deployments, run audits, manage user environments, and act as primary on-call responder to ensure continuous uptime and security.

Design, build, and maintain scalable, fault-tolerant backend services and data pipelines for an AI-driven cybersecurity platform. Implement secure APIs, optimize performance across distributed systems, establish backend best practices and CI/CD, write well-tested Python code, and mentor junior engineers.

Design and implement advanced Generative AI models, optimize performance for LLMs, and collaborate with cross-functional teams to innovate AI solutions within the automotive sector.

Design and build operational data systems, including pipelines, storage layers, and APIs for near real-time enforcement and insights, balancing streaming and batch paradigms.

The Senior Machine Learning Engineer will develop and optimize AI image generation models, monitor production issues, and collaborate on ML solutions for generative features.

Design, build, and deploy ML models across diverse use cases; work with managed cloud ML platforms and containerized solutions; collaborate with engineers on production integration and MLOps best practices; preprocess and analyze large datasets; mentor teammates and influence technical direction; translate complex ML concepts for technical and non-technical stakeholders.

The Software Engineer will design and build systems for sharded PostgreSQL, collaborate with a team to solve distributed systems challenges, and directly engage with customers to create technical solutions.

As a Software Engineer on the Insights team, you'll develop observability products and enhance database performance monitoring for clients using Vitess, MySQL, and PostgreSQL.

Lead end-to-end client ML engagements: scope problems, design, build, and deploy production ML solutions across cloud and containerized stacks. Lead and mentor small teams, manage client relationships, evaluate emerging tools, and ensure models are production-ready and explainable to technical and non-technical stakeholders.

As a Senior Full-stack Developer, you'll code back and front ends for the Drag Me apps, maintain app data, identify bugs, and improve operations while collaborating with a cross-functional team.

The Senior Backend Engineer will design and maintain backend services for core business functions, ensuring reliability and scalability while collaborating with cross-functional teams.

As a Staff Forward Deployed Engineer, you'll bridge technical gaps between large models and clients, ensuring successful deployments in drug discovery and simulations.

Design and deploy AI-powered workflow solutions, conduct requirements discovery, implement integrations, and build relationships with customers.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Principal/Staff Engineer will lead the technical direction of the platform, overseeing systems architecture, driving complex initiatives, and ensuring scalability and reliability while mentoring the engineering team.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

Lead liquidity forecasting, scenario analysis and credit facility management for Northleaf funds. Recommend financing strategies, optimize borrowing capacity, manage third-party providers, build reporting and automation, and mentor junior team members to support efficient capital deployment across private equity, private credit and infrastructure funds.

The Lead Quality Engineer will set quality standards, mentor a QA team, improve testing strategies, and enhance automation in AI-driven workflows.

Design, prototype and deliver agentic AI solutions and automations using Microsoft Copilot, Copilot Studio and Power Platform. Partner with business teams to map processes, build production-ready agents, enable integrations, and create reusable templates and orchestration patterns to drive adoption and enterprise-scale automation.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The ICA Engineering Manager will develop technical expertise in automation and IT/OT integration, support implementation of control and data systems, ensure compliance with safety and quality standards, and contribute to continuous improvement initiatives.