Top Python Jobs in Toronto

Lead architecture, product definition, design, fabrication, testing, and validation of silicon photonic integrated circuits for coherent optical transport systems. Develop link budgets, optimize system performance, and establish design-for-test and design-for-manufacturing strategies. Collaborate across photonics, RFIC/EIC, DSP, packaging, foundry, testing, and product teams to deliver scalable, manufacturable optical engines and integrated transceiver solutions.

Lead threat intelligence activities focused on identifying emerging vulnerabilities and exploitation, driving zero-day alerts, creating detection rules and technology fingerprints, maintaining honeypot networks, analyzing security telemetry, reducing false positives, and publishing actionable advisories. The role collaborates with security, underwriting, incident response, product, and data engineering teams, participates in emergency on-call coverage, and translates vulnerability research into enterprise risk signals.

The Senior Geochemist will serve as an embedded geochemical expert across international mineral exploration programs. Responsibilities include designing surface and drilling sampling programs, managing quality control, interpreting multi-element datasets, evaluating legacy data, supporting exploration targeting, training field teams, and collaborating with geologists, data scientists, and software engineers. The role requires extensive international travel, field deployment, technical judgment across multiple deposit types, and contributions to geochemical methods and technology development.

Leads enterprise application architecture, technical design, development, code reviews, deployment, production support, and software delivery. Oversees internal and outsourced developers, manages vendors, guides Agile planning, establishes development standards, and ensures project milestones, budgets, and quality goals are met. The role requires hands-on development, enterprise systems integration, database expertise, and experience with C#, Angular, Python, JavaScript, Azure DevOps, SaaS platforms, and software development lifecycle methodologies.

Embed with healthcare customers to understand workflows, design and deploy AI-powered solutions, build production systems and integrations, and own deployments from scoping through iteration. Translate ambiguous business requirements into reliable technical architectures, collaborate with Product and Engineering, monitor systems, and identify deployment patterns that inform scalable product and platform improvements.

Senior Software Engineer to design and implement C++/Python software, accelerate workloads with CUDA, develop and maintain Linux kernel modules and system services, optimize system and GPU performance, manage Linux systems, mentor junior engineers, and integrate AI technologies across products.

Leads globally distributed engineering teams delivering customer-facing SaaS capabilities. Owns engineering execution, architecture, modernization, reliability, operational excellence, AI adoption, talent development, staffing, and stakeholder alignment. Manages Engineering Managers, Senior Managers, and Principal Engineers while guiding distributed systems, cloud-native services, APIs, data-intensive platforms, CI/CD, security, and production operations. Builds India-based engineering leadership and promotes predictable delivery, technical quality, and measurable business outcomes.

Designs and maintains scalable build and deployment pipelines, enforces release quality gates, resolves environment and infrastructure issues, and integrates AI agents into CI/CD workflows for automated testing, log analysis, production monitoring, and incident triage.

Design, build, evaluate, and deploy LLM-powered agents and machine learning systems that improve Quora content quality and safety. The role involves developing agentic workflows, conducting offline and online evaluations, monitoring production performance, investigating failures, and improving accuracy, reliability, latency, and cost. New graduates will collaborate with experienced engineers and own projects from development through production.

Leads quantitative modeling and development for counterparty credit risk and XVA trading. Responsibilities include developing and maintaining pricing and risk models, supporting traders, analyzing market and trade data, producing reports, and collaborating with risk oversight, model validation, product control, senior management, and trading teams. The role requires advanced quantitative modeling expertise, programming skills, and experience implementing XVA, credit, or counterparty credit risk models.

Reviews market data and OTC derivatives valuations, resolves pricing challenges, manages valuation processes, supports client relationships, contributes to valuation-system design and testing, and improves data capture, cleaning, and automation across multiple asset classes.

Build and operate production-grade AI agents that automate workflows across internal teams. Design agent architectures with LangGraph and Amazon Bedrock AgentCore, integrate internal systems and third-party tools, and develop evaluation, observability, and guardrail frameworks. Partner with nontechnical teams to identify workflows, own solutions from prototype through production, measure impact, and drive adoption through documentation and training.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Lead marketing analytics: own reporting frameworks, dashboards, and data quality; monitor KPIs; run A/B tests and experiments; build attribution, forecasting, segmentation, and budget-allocation models; automate workflows and drive continuous measurement improvements.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Build and own user-facing features and core platform architecture across frontend, backend, and ML pipelines. Integrate AI workflows (LLMs, embeddings) for curriculum generation and automated evaluation. Manage end-to-end development: requirements, design, implementation, CI/CD, testing, observability, and iteration while collaborating with product, design, ML, and customer teams.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.

Implement customer projects using Python, machine learning, and deep-learning tools for engineering, physics, CAD, CAE, and manufacturing workflows. Develop proofs of concept, analyze project data, train customer teams, and translate customer feedback into product improvements. The role requires strong engineering and machine-learning knowledge, simulation experience, customer-facing communication, and travel to US customer locations.

Develop and productionize high-performance LLM inference techniques across runtimes, scheduling, serving, routing, and GPU kernels. Profile and optimize latency, throughput, memory usage, and cost; support new models and hardware; build benchmarking frameworks; and contribute to open-source inference engines such as vLLM, SGLang, and TensorRT-LLM. The role requires strong programming, GPU architecture, ML library, and LLM optimization knowledge.

Drive quality assurance for web features from technical handoff through production release. Responsibilities include test planning, test case creation, functional and exploratory testing, regression, UI/UX, cross-browser, responsive, API, and end-to-end testing. Support releases through build validation and monitoring, document and review tests, collaborate with product and engineering teams, improve QA metrics, and optionally contribute to test automation.

Build and productionize real-time conversational AI systems using voice, chat, LLM orchestration, retrieval, and multi-channel session management. Own services from architecture through Kubernetes/Azure deployment and operations. Develop evaluation infrastructure, tune LLM pipelines for quality, latency, and cost, integrate AI and voice vendors, and establish engineering practices for a growing team. Partner with product and compliance stakeholders to encode domain rules into reliable AI systems.

Develop and maintain full stack applications for Citi’s Oracle Ledger Program using Java, Angular or React, REST APIs, and Selenium. Lead software delivery from analysis through implementation, provide advanced L3 production support, resolve complex issues, collaborate with business and technology teams, uphold engineering standards, and mentor developers within a global delivery model.

Own NimbleRx’s end-to-end data platform, including ingestion, transformation, storage, querying, access, batch and streaming pipelines, warehouse modeling, security, compliance, and reliability. Optimize PostgreSQL and Java/Spring services, investigate infrastructure issues, create self-service documentation and tooling, apply AI to data workflows, and mentor engineers and analysts. The role partners broadly across product, engineering, analytics, and operations while driving the platform roadmap.

Designs, develops, tests, and deploys middleware, APIs, connectors, protocol translators, and scripts integrating legacy applications with Okta and SailPoint IdentityIQ. Builds identity provisioning, authentication, authorization, and access-control solutions using SAML, OAuth, OIDC, and SCIM. Analyzes legacy systems, supports application onboarding, performs security testing, maintains technical documentation, and ensures compliance with DoD security, accessibility, and supply-chain requirements.

Lead the technical direction of a cloud-based USDA environmental review platform. Oversee full-stack development, APIs, cloud infrastructure, integrations, security compliance, testing, performance, CI/CD pipelines, documentation, and transition planning. Guide technology decisions and development practices while collaborating with product, facilitation, and design teams. The role requires federal IT project leadership, cloud-native development, agile experience, and the ability to obtain Public Trust.