Top Python Jobs in Toronto

Operate and maintain Kepler's satellite constellation, perform LEOP and commissioning, resolve anomalies, analyze performance, develop/improve procedures and automation tools, support launches, and participate in on-call shift rotations to ensure continuous service.

Lead QA strategy and execution for web, API, and mobile. Establish Playwright automation, integrate AI-assisted testing into CI/CD, mentor QA engineers, improve regression stability and coverage, define release validation standards, and track quality KPIs.

Senior individual contributor who architects, prototypes, and ships production-grade AI solutions for Finance. Focus areas include agentic AI, multi-agent orchestration, ML forecasting and anomaly detection, RAG and vector-store solutions, responsible AI and governance, and embedding AI into FP&A processes. Partners with engineering, IT, security, and finance teams to productionize systems and establish standards, monitoring, and controls.

Designs and executes testing and measurement for retail and brand partners: data extraction, modeling, attribution/incrementality analysis, BI visualization, and delivering actionable insights. Mentors junior analysts and collaborates cross-functionally to define data requirements and improve measurement practices.

Lead design, deployment, and operations of enterprise and hybrid Azure networks across multiple sites. Maintain LAN/WAN/MPLS/BGP/OSPF, FortiGate firewalls, SD‑WAN/SASE, ExpressRoute/VPN connectivity, DR networking in Azure and on‑prem, network security/segmentation, observability (NetFlow/SNMP), and infrastructure lifecycle activities while supporting 24×7 operations and cloud migration projects.

Lead and deliver an enterprise AI platform and agent factory: design architecture, build cost-effective model infrastructure, harden security/SSO/IAM, enable developer adoption, and build agent workflows across the PDLC (ideation, development, deployment). Mentor engineers, steer delivery partners, and remain hands-on implementing agents, RAG pipelines, integrations, and observability to drive measurable productivity gains.

The engineer will design, characterize, and develop coherent optical transceivers, focusing on digital-optical integration and performance optimization.

Build and own a versioned, documented gold data layer from lakehouse silver tables for AI research. Transform, validate, and productize multi-modal datasets and pipelines (Databricks/PySpark) to support model development, evaluation, and production ML/LLM workflows while ensuring data quality, provenance, and regulatory controls.

Senior-level role building and validating real‑world datasets and analyses. Responsibilities include programming (SAS/R/Python/SQL), cohort derivation, index date definition, applying GLM/logistic/Cox and propensity score methods, working with claims/EHR sources (Optum, HealthVerity, PharmMetrics), OMOP transformations, protocol/SAP review, QC/validation, documentation, and mentoring. AI/ML and LLM workflow usage is a plus.

Design, build, and operate production-grade generative and agentic AI applications and services. Develop Python backend services, RAG pipelines, multi-agent systems, LLM integrations, APIs, and cloud deployments. Implement observability, security, and engineering best practices while mentoring junior engineers and driving platform architecture decisions from prototype to production.

Design, build, and operate production-grade Generative and Agentic AI applications and services. Develop Python backend services, integrate cloud-hosted LLM APIs, implement RAG pipelines and vector search, build multi-agent systems, deploy on major cloud providers, ensure observability, security, and performance, and mentor junior engineers.

Join the Trading Engine team to develop and maintain client-facing open-source libraries, act as an SME for REST and WebSocket trading APIs, and work directly with large clients to design and optimize trading algorithms and integrations.

Design, evolve, and operate Shakepay's internal developer platform and tooling. Build infrastructure-as-code, GitOps workflows, observability, AI-powered automation, and scalable systems. Lead cross-team technical initiatives, mentor engineers, drive reliability and operational excellence, and contribute to incident response and platform strategy.

Lead analytics and risk measurement at Shakepay: define risk metrics and decision frameworks, analyze fraud/scam patterns, build reporting, design data instrumentation and decisioning systems, evaluate controls with statistical and selective ML methods, conduct investigations, and communicate insights to influence engineering and product priorities.

Lead design, development, and implementation of data integration solutions for Oracle EPM Cloud (Planning, FCC, Reconciliation). Build ETL processes, configure DE/DM/OIC, perform data mapping/validation/testing, provide technical support and training, lead project teams, and recommend optimizations to ensure data accuracy and integration performance.

Design and implement risk measurement, decisioning, and detection systems. Analyze fraud, scam, and abuse patterns; build reporting and instrumentation; evaluate risk controls with statistics and selective ML; conduct deep-dive investigations; partner with engineering and cross-functional teams to improve data quality, decision frameworks, and operational tooling.

Design and architect large-scale distributed Big Data solutions using Java, Spark, and Hadoop/Cloudera. Lead global teams to build high-volume data processing and analytics pipelines, ensure robust testing (JUnit), and apply scripting for automation and data analysis.

Senior Java Big Data Developer with 8+ years building applications on Cloudera/Hadoop ecosystems using Spark and MapReduce. Responsible for designing performant Java (1.8+) solutions, data analysis of large datasets, unit testing (JUnit), and collaborating with/managing global technology teams. Preferred skills include Unix/Python scripting and familiarity with Hive, Impala, YARN, and Kafka.

Lead design and implementation of GenAI platform components and secure APIs. Drive end-to-end AI development from research to production, apply DevOps for scalable deployments, collaborate with cross-functional teams, and stay current with GenAI advances to deliver customer value.

Design, develop, fine-tune, and optimize generative deep learning models (GANs, VAEs, transformers). Collaborate with cross-functional teams to integrate and deploy AI solutions, troubleshoot model issues, document models, and communicate technical concepts to non-technical stakeholders.

Build and maintain responsive Angular UI components, collaborate with UX and backend teams, implement REST APIs and Python backend scripts, integrate basic ML models into data workflows, and participate in the full SDLC within Agile teams while ensuring code quality and performance.

Build and maintain platform services for user lifecycle and HRIS integrations that process millions of records. Design, implement, and operate scalable, reliable microservices, APIs, and event-driven systems using cloud and container technologies. Contribute to design and code reviews, troubleshooting, migration from monoliths, estimation, and mentoring junior engineers.

Lead design, build, and maintenance of User Domain and HRIS integration services handling millions of user records. Architect scalable, secure microservices, mentor engineers, own features end-to-end, collaborate cross-functionally, and improve performance, reliability, and tooling.

Design, train, evaluate, and deploy predictive transportation and delivery ML models (primarily XGBoost). Perform EDA, feature engineering, experiment design, and statistical evaluation. Build production ML pipelines and MLOps practices on Google Vertex AI. Collaborate with data engineering and supply chain stakeholders to translate operational needs into scalable solutions.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.