Top Python Jobs in Toronto

Design, develop, test, maintain, and support enterprise software applications, APIs, services, integrations, and user-facing functionality. Translate requirements into technical designs, write maintainable code, participate in Agile planning and code reviews, troubleshoot production issues, and collaborate with analysts, QA, architects, and operations teams. Contribute to automated testing, technical documentation, production support, and continuous engineering improvement.

Develop secure, scalable, production-ready AI applications, services, and APIs. Integrate machine-learning and generative AI models into enterprise systems, build data and retrieval workflows, develop prompts, and implement testing, monitoring, logging, and evaluation. Collaborate with product, data, platform, and engineering teams while applying security, privacy, responsible-AI, and governance practices. Troubleshoot issues, document technical designs, participate in code reviews and Agile ceremonies, and support continuous improvement.

Lead software quality for autonomous vehicle planner architecture, developing fault-tolerant systems and improving reliability across motion planning and onboard software teams. Design motion planning software and decision-making algorithms for complex traffic scenarios, while managing experimentation and data-generation pipelines. Collaborate with multidisciplinary engineering and research teams to deliver safe, production-quality self-driving technology.

Lead design, development, and deployment of large-scale Python-based Generative AI platforms and services. Drive architecture, stakeholder engagement, vendor partnerships, CI/CD, and governance. Manage multiple complex projects, mentor mid-level engineers, and ensure risk, monitoring, and integration across Citi business units.

Support pre-sales through product demonstrations, value proposition development, demo environment design, requirements gathering, documentation, presentations, and RFP/RFI responses. Act as the liaison between Sales and Financial Engineering, translating prospect needs and coordinating technical resources. The role requires expertise in wealth, portfolio, or risk management, strong presentation skills, and the ability to explain complex financial technology concepts to prospects and clients.

Leads Sales and Marketing Analytics by managing analysts, setting the analytics roadmap, and guiding measurement strategy across acquisition, demand generation, sales, partnerships, and revenue funnels. Owns frameworks for attribution, incrementality, forecasting, marketing mix modeling, CAC, LTV, and ROI. Partners with senior leaders and data teams to deliver trusted reporting, executive insights, self-serve analytics, automation, and responsible AI-powered decision support.

Lead enterprise Generative AI strategy and engineering: select and deploy LLMs, design RAG and agentic systems, build production GenAI services, establish governance, observability, cost controls, and reusable accelerators while managing senior engineering teams and cross‑functional stakeholders.

Build and operate ingestion, reconciliation, and reporting systems that reconcile card-network and partner settlement files against internal transactions. Deliver end-to-end features for traceability, accounting journals, tax and regulatory reporting, and compliance tooling while ensuring reliability, scalability, and data privacy.

Leads a sizable software engineering organization building and modernizing scalable customer runtime infrastructure for Drupal, WordPress, and Next.js sites. Responsibilities include people leadership, architecture guidance, project delivery, technical strategy, cross-functional collaboration, performance management, and cloud infrastructure modernization. The role requires deep expertise in distributed systems, cloud platforms, Kubernetes, Linux fleets, Infrastructure-as-Code, databases, caching, and modern programming languages.

Lead a team to design, deploy, and maintain Industrial IoT and cloud-connected smart factory solutions. Build AWS-based, AI-enabled workflows, manage IaC (Terraform), CI/CD, monitoring, and collaborate with cross-functional teams to improve manufacturing performance.

Build, maintain, and evolve Mozilla’s Internal Developer Infrastructure Platform: CI/CD, observability, Kubernetes optimization, environment bootstrapping, and cost optimization. Lead technical decisions, create Claude skills and agentic workflows to boost developer productivity, mentor teammates, write proposals, and deliver cross-functional platform features while ensuring usability, maintainability, and safe production paths.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.

Build and evolve scalable frontend, backend, and shared platform services. Define platform boundaries with product engineering teams, navigate ambiguous requirements, architect reliable systems, improve performance and observability, and promote quality engineering practices. Lead AI-assisted development adoption, coach engineers, influence technical direction, and collaborate with Product, Design, and engineering leadership.

Develops, validates, and maintains clinical data programming solutions for CDMS and clinical trials, including eCRFs, edit checks, data validation outputs, reports, extracts, transfers, and vendor data programs. Collaborates with cross-functional clinical data teams, supports study setup and maintenance, mentors junior programmers, creates training materials, and improves technical systems and processes.

Design, develop, deploy, and optimize generative AI applications using LLMs and AI frameworks. Build scalable APIs, integrate foundation models and third-party platforms, automate business workflows, and connect AI solutions to internal systems and data sources. Monitor model quality, improve prompts and configurations, support responsible AI governance and security, evaluate emerging technologies, and collaborate with product, engineering, and business teams.

Leads the architecture, development, optimization, deployment, and lifecycle management of enterprise machine learning systems. Establishes AI engineering standards, MLOps practices, data quality processes, model governance, security, and validation frameworks. Provides technical leadership, mentors engineers, evaluates emerging technologies, and supports AI transformation initiatives while partnering with data scientists, engineering, product, and business teams.

Designs and maintains Retrieval-Augmented Generation systems, including document ingestion, indexing, semantic search, vector databases, embedding pipelines, and LLM integrations. Develops APIs and scalable knowledge systems, optimizes retrieval accuracy and performance, monitors production environments, and implements governance and security controls. The role also evaluates emerging retrieval technologies, troubleshoots indexing issues, documents procedures, and collaborates with AI engineers and product teams.

Leads the design, development, and optimization of enterprise Retrieval-Augmented Generation platforms. Responsibilities include building retrieval architectures, ingestion and indexing frameworks, semantic and vector search systems, evaluation methods, scalable APIs, and LLM integrations. The role also covers security, governance, compliance, monitoring, troubleshooting, documentation, technical leadership, mentoring, and evaluating emerging AI retrieval technologies.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering and evaluation strategies, and ensures secure, scalable production deployments. Partners with executives and business leaders to define AI roadmaps, mentors engineering teams, evaluates emerging technologies, and drives proof-of-concept initiatives and responsible AI practices.

Designs, tests, evaluates, and refines prompts for generative AI applications across customer support, content generation, workflow automation, knowledge management, and productivity use cases. The role evaluates AI outputs for accuracy, relevance, tone, and consistency; documents testing results; maintains prompt libraries and guidelines; collaborates with AI engineers, product teams, data scientists, and business stakeholders; and researches emerging AI tools and prompting techniques.

Manage and optimize infrastructure supporting AI and machine learning systems, including cloud environments, GPU/CPU clusters, model deployment, data pipelines, security, monitoring, troubleshooting, automation, and CI/CD. The role develops scripts, maintains documentation, supports AI teams, and improves system reliability and scalability. Candidates need a relevant bachelor's degree, at least two years of systems administration or infrastructure experience, cloud and containerization expertise, scripting skills, and familiarity with machine learning frameworks and deployment pipelines.

Develop, deploy, and optimize scalable machine learning solutions, including predictive, deep learning, and intelligent automation models. Build data pipelines, prepare large datasets, monitor production models, manage retraining, and ensure quality, security, compliance, and responsible AI practices. Collaborate with data scientists, engineers, product teams, and stakeholders while researching emerging AI technologies and improving AI architectures.

Own the full lifecycle, security, compliance, and support of company endpoints across macOS, Windows, Linux, and mobile devices. Administer UEM and Okta, deliver escalated IT support through Jira Service Management, manage onboarding and offboarding, maintain asset records, enforce endpoint security controls, automate provisioning and triage with AI, and partner with Information Security on audits, access reviews, and least-privilege policies.

Design, build, and operate CIAM backend services for registration, authentication, authorization, and account lifecycle. Implement OAuth/OIDC/SAML/SCIM, develop APIs in Python/Kotlin, integrate identity platforms, automate deployments with IaC and CI/CD, and ensure secure, reliable, high-scale identity operations.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.