Top Python Jobs in Toronto

Leads end-to-end enterprise data architecture, engineering, governance, delivery, and production readiness. Designs and builds Azure Data Factory, Azure SQL, Power BI, Logic Apps, Python, and Databricks solutions; establishes data layers, controls, lineage, reconciliation, testing, and operational standards. Troubleshoots complex issues, coordinates stakeholders, reviews code, coaches junior staff, manages production transition, and develops the automation roadmap.

Lead technical strategy and architecture for Coinbase Payments: design and evolve high‑availability, low‑latency funds‑flow and rails systems, drive cross‑team initiatives and reliability improvements, enforce financial correctness, mentor senior engineers, and align stakeholders across product, risk, and compliance.

Own the architecture and evolution of test automation frameworks for Guidewire Cloud and AI-powered services. Build API, UI, data, contract, and performance automation; integrate testing into CI/CD; and develop AI-driven testing capabilities. Establish standards for distributed, containerized environments using Docker and Kubernetes, improve observability and reliability, define quality objectives, and mentor engineers across teams. The role requires deep expertise in software testing, distributed systems, cloud platforms, and automation architecture.

Close inbound deals with technical customers, manage proofs of concept, collaborate with solutions engineers, support customers in Slack, answer technical questions honestly, and improve internal sales systems. The role requires technical literacy, systematic pipeline management, strong written communication, urgency, independence, experimentation, and customer-focused judgment.

Design and develop miniaturized analog and mixed-signal PCBs for wearable biosignal recording and electrical-stimulation medical devices. Responsibilities include schematic capture, PCB layout, component selection, power and RF design, high-voltage circuitry, board bring-up, embedded C firmware, test automation, laboratory debugging, design verification, manufacturing support, documentation, and compliance activities. The engineer will collaborate with firmware, mechanical, textile, systems, and clinical teams throughout product development.

Conduct applied research on foundation models for real-time fraud detection using large-scale behavioral and sequential data. Design experiments, benchmarks, holdouts, and monitoring systems; develop models from data preparation through deployment; and optimize training and inference efficiency. Collaborate with engineering, client-facing teams, legal, compliance, and customers to productionize models and establish explainability, governance, and risk documentation.

Designs, models, integrates, tests, and validates electro-optical subsystems for high-speed optical communication systems. The role develops requirements, link budgets, signal integrity analyses, test methodologies, and technical documentation; performs laboratory characterization and root-cause analysis; leads integration activities; and supports product qualification and production readiness in collaboration with optical, hardware, DSP, FPGA, firmware, manufacturing, and product engineering teams.

Build and deploy AI agents that automate internal business processes. Responsibilities include authoring reusable agent skills, integrating systems through MCP, creating evaluation sets, deploying production workflows with permissions and audit trails, and partnering with data engineers. The role requires translating business processes into reliable agent workflows, supporting deployed agents, and maintaining operational quality using Python, SQL, Git, CI, and containers.

Own end-to-end Lyric deployment workstreams from planning through go-live. Manage project plans, meetings, timelines, budgets, risks, UAT, customer adoption, training, and post-launch monitoring. Partner with executives, engineers, and supply chain operators to translate business needs into platform solutions and ensure successful delivery.

Responsible for designing experiments to evaluate AI models, developing evaluation methodologies, analyzing performance data, and collaborating on production insights.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

Designs, builds, and evaluates language models for AI security products. Responsibilities include dataset curation, preprocessing, LLM experimentation, safety and robustness evaluation, dashboards, knowledge graphs, model fine-tuning, production integration, monitoring, CI/CD, and ML tooling. Collaborates with researchers and data scientists to automate quality assurance and guardrails while delivering reliable, scalable, and interpretable models.

Own full-cycle recruiting for engineering, AI/ML, data, and product roles across multiple markets. Develop sourcing strategies, technically screen candidates, manage requisitions in Greenhouse, partner with hiring leaders, run structured interviews, negotiate offers, and identify pipeline or process risks. This is a highly autonomous six-month remote contract requiring strong technical recruiting expertise and experience managing senior-level searches.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Owns enterprise AI architecture across AWS, Azure, Microsoft 365, and SaaS platforms. Defines target-state architecture, standards, guardrails, and governance; aligns engineering teams and senior stakeholders; oversees AI platform capabilities, Microsoft AI surfaces, security, DevOps, data, responsible AI, and regulatory compliance. Evaluates emerging AI technologies, supports architecture governance, mentors senior architects and engineers, and ensures production systems generate model-risk and regulatory evidence.

Validate and integrate optical coherent pluggable firmware for development releases and customer support. Responsibilities include manual and automated testing of CMIS-compliant modules, Python test automation on the PACE platform, test report analysis, bug investigation, lab equipment setup, interoperability verification, documentation, and collaboration with scrum and cross-functional teams. The role requires on-site Ottawa lab support at least three days per week.

Own quality engineering for high-throughput payment systems by architecting automated regression, performance, stress, and load testing. Build emulators for external payment systems, integrate quality gates into CI/CD pipelines, validate microservices, event-driven architectures, and databases, and use Prometheus and Grafana for observability. Apply AI-assisted testing, synthetic data generation, and edge-case analysis while mentoring engineers and preventing incidents through telemetry-driven testing and post-mortems.

Own quality engineering for high-throughput payment systems by designing automated regression suites, performance and load tests, quality gates, external-system emulators, observability-driven test environments, and AI-assisted testing workflows. Partner with feature teams and product leaders to improve reliability, scalability, security, and transactional accuracy. Mentor engineers, support incident prevention, and embed automated validation throughout the software development lifecycle.

Provides operational and administrative support to wealth management clients using the d1g1t platform. Responsibilities include client support, platform configuration, user onboarding, reconciliation, software testing, documentation, training, reporting, dashboard development, and identifying product improvements. The role works closely with clients and product teams while analyzing and organizing financial data using spreadsheets, SQL, and Python.

Leads the architecture, development, optimization, and operation of large-scale batch and real-time data platforms using Spark, Databricks, Kafka, and lakehouse technologies. Designs petabyte-scale pipelines, tunes Spark and SQL performance, manages governance and monitoring, establishes data-modeling and operational standards, and leads a global data engineering team. Advises stakeholders, mentors engineers, manages delivery risks and dependencies, and ensures production readiness, data quality, compliance, and support.

Build and scale ML-optimized HPC infrastructure, manage Kubernetes-based GPU/TPU superclusters, optimize for AI/ML training, and mentor teams while innovating in ML infrastructure.

Build and operate Planet’s large-scale storage infrastructure, including object storage, metadata indexing, Elasticsearch search, event notification, and disaster recovery. Develop highly available backend services and APIs, monitor and tune production systems, respond to incidents, and improve performance. Advise engineers, lead technical projects, contribute to architectural roadmaps, participate in design reviews, and support engineering practices and documentation.

Leads and mentors data engineering teams while designing, deploying, and operating scalable data pipelines and platforms supporting analytics, AI/ML, and commercial use cases. Provides architectural leadership across cloud-native platforms, distributed processing, orchestration, data integration, and streaming architectures. Manages delivery priorities, stakeholder requirements, operational excellence, incident response, standards, and continuous improvement across enterprise data engineering initiatives.

Build reliable AI-powered, agentic backend systems to remove friction across the software development lifecycle. Design multi-step orchestration, safety checks, and evaluation tooling; apply distributed-systems judgment; work across GitLab codebases and with a distributed team to productionize solutions that may become customer-facing.

Lead technical direction for a greenfield team building agentic/LLM-backed systems to reduce SDLC friction. Drive discovery through shipping, apply distributed-systems judgment at scale, mentor engineers, own technical hiring and cross-team alignment, and evaluate correctness and safety of agentic workflows.