Top Python Jobs in Toronto

Design and build Index Cloud, a globally distributed, multi-tenant compute platform across Kubernetes, bare-metal, and public cloud infrastructure. Own architectural decisions, infrastructure-as-code frameworks, platform APIs, SDKs, deployment systems, networking, storage, and distributed systems solutions. Drive technical strategy through RFCs and design reviews, improve developer productivity with self-service tooling, and mentor engineers across multiple teams.

Design and build Index Cloud, a globally distributed, multi-tenant platform operating across bare-metal and public cloud infrastructure. Responsibilities include architecting Kubernetes clusters, infrastructure-as-code frameworks, deployment systems, platform APIs, SDKs, and distributed systems. The role drives technical strategy through RFCs and design reviews, improves developer self-service, establishes security and tooling standards, mentors engineers, and collaborates across SRE, networking, security, operations, and software engineering teams.

Develop backend Trust and Safety tools, features, and distributed systems that protect Lyft riders and drivers. Lead safety features from concept through launch, collaborate with product, design, policy, and engineering partners, write maintainable and tested code, participate in reviews and on-call rotations, and promote engineering best practices through knowledge sharing.

Develop and maintain LLVM/MLIR compiler analysis and transformation passes for high-level FPGA synthesis. Optimize generated hardware for performance, power efficiency, and area utilization; analyze customer applications; and implement HLS features. Contribute to SoC design, verification, hardware/software co-design, RISC-V integration, and FPGA-based accelerator workflows.

Conduct frontier research on data-efficient agentic AI, including model architectures, meta-learning, test-time adaptation, and reinforcement learning. Train deep learning models at scale, deploy agentic pipelines, design experiments and evaluations, analyze results, communicate findings, participate in brainstorming, support academic papers and technical reports, and stay current with machine learning publications.

Own and evolve cloud, on-premises, and edge infrastructure supporting AI, data, and product workloads. Design Kubernetes platforms, GPU scheduling, CI/CD pipelines, infrastructure-as-code, observability, security, and reliability practices. Lead infrastructure projects, incident response, platform strategy, and developer enablement while balancing cost, latency, reliability, and velocity. Mentor engineers and provide technical direction for scalable, production-grade ML and industrial systems.

Own SOC 2 Type 2 and ISO 27001 compliance programs while securing AWS, GCP, SaaS, infrastructure, identity, CI/CD, and software supply chains. Triage and remediate security findings, administer access and endpoints, support IT operations, manage auditors and customer reviews, debug cloud production issues, and build automated security guardrails, documentation, and postmortems.

Design, train, evaluate, and deploy production computer vision systems for industrial applications. Build detection, segmentation, classification, OCR, tracking, and visual-language pipelines; optimize real-time inference for edge and cloud environments; develop data and evaluation pipelines; fine-tune multimodal foundation models; and improve performance through quantization, pruning, and hardware acceleration. Collaborate across AI, engineering, product, and research teams on architecture, technical design, and production-grade AI systems.

Design and operate production backend services and AI infrastructure, including model serving, inference, data pipelines, vector stores, RAG systems, APIs, and microservices. Architect scalable real-time and batch systems, integrate and deploy LLM, computer vision, and machine learning models, and ensure reliability, observability, testing, and CI/CD. Collaborate with ML, product, and customer-facing teams while documenting systems and mentoring engineers.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Build and prototype enterprise AI solutions, including agents, MCP integrations, Copilot extensions, and automations. Partner with business and technical stakeholders to identify valuable use cases, evaluate emerging AI technologies, and move successful proofs of concept toward production. Maintain reusable AI assets, document solutions for engineering handoff, advise leadership on AI strategy, and support responsible, secure, scalable adoption across the organization.

12-month rotational role supporting fund administration and auditable reporting services. Use AI tools, scripting, and CLI workflows to automate financial processes, support 409A valuations, tax reporting, fund implementations, and portfolio data collection, and provide feedback to product and engineering.

Leads QuadReal’s Data Solutions team and owns data modeling, analytics engineering, and business intelligence capabilities within the Enterprise Data Platform. Responsibilities include team development, roadmap and delivery management, stakeholder engagement, technical standards, data quality, governance, and vendor accountability. The role translates business and finance requirements into trusted data models and BI products using MS Fabric, dbt, Airflow, Power BI, and related technologies.

Lead PAM architecture and engineering for IDIRA/CyberArk Privileged Cloud, including custom CPM plugins, PSM connectors, MFA integrations, JIT access, cloud administration, and AI-agent credential vaulting. Establish onboarding standards, security controls, automation, auditability, and risk metrics across AWS, Azure, and GCP. Troubleshoot complex platform issues, remediate privileged-access risks, guide application teams, and support incident response involving compromised privileged accounts.

Leads the design, development, deployment, and scaling of AI, machine learning, generative AI, and agentic AI solutions for commercial pharmaceutical operations. Owns the full AI product lifecycle, advises on architecture and modeling, partners with cross-functional stakeholders, ensures responsible AI governance, develops reusable frameworks, communicates insights, mentors technical staff, and supports organizational AI strategy and capability building.

Designs, develops, deploys, and supports production-grade Generative AI and Agentic AI solutions. Builds RAG systems, multi-agent architectures, AI copilots, reusable AI frameworks, orchestration workflows, and enterprise integrations. Collaborates with product, business, data, and engineering teams; ensures scalability, reliability, observability, security, privacy, governance, and regulatory compliance. Evaluates and improves AI applications, contributes to platform strategy, communicates technical decisions, and mentors junior technical staff.

Own and scale client-facing data transformation pipelines for financial and operational data. Build reliable analytical models, data integrity tests, and integrations across diverse sources. Partner with Customer Success and Engineering to troubleshoot data issues, optimize critical pipelines, establish engineering standards, and improve developer experience. Use dbt, SQL, Python, BigQuery, and AI-enabled workflows to deliver performant, well-documented FP&A data products while mentoring teammates and contributing to hiring and onboarding.

Build AI-powered features from prototype through production, design and run evaluations, maintain data and inference pipelines, and improve model accuracy, latency, and cost. The role involves experimenting with LLMs, prompting, fine-tuning, retrieval approaches, and vector databases, while collaborating closely with teammates and owning work end-to-end.

Designs and maintains Azure-based data lakehouse architecture, batch and real-time ETL pipelines, and data migration workflows from EHR, legacy, and business systems. Builds OLTP and OLAP data models, integrates Salesforce Health Cloud, transforms HL7, FHIR, and EDI data, and implements validation, deduplication, encryption, masking, and PHI-compliant security. Develops high-performance pipelines using Informatica, Azure services, Python, SQL, Spark, and related technologies to support clinical analytics, business intelligence, and AI initiatives.

Build full-stack AI and data-platform applications, including Python backend automation, agent-enabled workflows, MCP servers, metadata and data-contract tooling, and platform discovery interfaces. Design APIs, asynchronous services, schemas, microservices, testing, and CI/CD pipelines while integrating LLMs, graph data, cloud infrastructure, and governance platforms. Contribute to architecture, scalability, security, code quality, and client-focused consulting delivery.

Own the realtime voice channel end to end, including telephony integrations, streaming speech pipelines, model orchestration, and latency optimization. Build full-stack features with React, TypeScript, Scala, Python, and GCP; design scalable multi-tenant systems; implement safety and compliance controls; maintain observability, incident response, and SLA performance; and mentor engineers through architecture and code reviews.

Lead the architecture and technical direction of Lyft’s mapping data platforms, including offline experimentation tooling, route simulation services, scalable pipelines, data governance, and observability. Own pipeline reliability and SLAs, evolve schemas, optimize SQL and data processing, build backend services and AI-assisted data tools, and mentor engineers. Collaborate with engineering, product, machine learning, and data science teams to improve routing quality and travel-time accuracy.

Owns the end-to-end architecture, governance, delivery, and production readiness of an enterprise data solution. Designs and develops Azure-based data pipelines, SQL databases, Power BI reporting, Logic Apps, and Python solutions. Establishes data layers, controls, lineage, reconciliation, testing standards, and operational service levels. Leads troubleshooting, code reviews, stakeholder coordination, production transitions, and automation planning while coaching junior team members.

Own end-to-end simulation, co-design, and laboratory verification of photonic-electronic links. Develop multi-physics optical and analog models, perform corner, Monte Carlo, and PVT analyses, create link budgets, and correlate simulations with silicon measurements. Partner with photonic and analog circuit teams on architecture, specifications, interface requirements, and corrective design actions. The role requires high-speed optical lab expertise, silicon photonics experience, and proficiency in photonic, mixed-signal, and Python-based simulation workflows.

Own complex analytics engineering workstreams, design scalable analytical models and semantic layers, establish modeling and quality standards, improve AWS workload performance and reliability, and support AI-driven analytics capabilities. Partner with platform, product analytics, engineering, and governance teams; guide migrations and technical debt reduction; ensure privacy, security, and business fit; and mentor analytics engineers and analysts.