Top Python Jobs in Toronto

Work directly with strategic customers to design, build, deploy, and operate production AI and data systems on Shakudo. Responsibilities include discovery, architecture, data pipelines, RAG/agent workflows, Kubernetes/cloud deployments, production support, governance, and driving adoption and measurable business outcomes.

Architect and deliver production-ready data and AI solutions on the Shakudo platform. Integrate Kubernetes/Helm, cloud, and IaC into customer environments, troubleshoot complex stack issues, drive adoption and account growth, and act as trusted technical advisor across engineering and executive stakeholders.

The Associate Client Analyst role involves supporting proxy voting work by reviewing documents, managing governance data, conducting research, and collaborating with teammates, all while developing knowledge in governance and proxy voting.

The role involves enhancing pricing sophistication, delivering actuarial support, improving processes, resolving complex problems, and guiding team efforts in pricing analysis.

Lead systems engineering for spacecraft and space-system programs: define architectures, translate mission requirements, manage interfaces and technical budgets, perform trade studies and risk analyses, drive verification and test campaigns, support integration/commissioning and operations, mentor engineers, and apply MBSE and AI-enabled tools to improve engineering workflows and deliver program technical products.

Support satellite and payload development by performing mission and system analyses, maintaining requirements and interface documentation, supporting payload integration, verification planning, testing, and operations readiness, coordinating cross-functional teams, and producing technical budgets and reports under senior engineer guidance.

Design and build backend services for complex enterprise workflows, own data models and service reliability, improve scalability and observability, partner with Product, participate in design/code reviews, and mentor peers.

Design, build, and maintain Spark-based data pipelines and platform services for sales compensation calculations. Improve data modeling, reliability, observability, testing, and scalability. Partner with Product, Engineering, and Analytics to translate business requirements into deterministic, production-grade data systems and participate in design and code reviews.

Build self-service ML platform tooling and golden paths enabling Data Scientists to deploy and monitor models across batch and real-time. Design CI/CD, model registry, deployment workflows, observability standards, SDKs/CLIs, and partner with staff MLEs to shape platform architecture and developer experience.

Own a scoped project in machine learning, AI infrastructure, MLOps, data, robotics, simulation, or world models. Work with production repositories, clusters, and data to deliver a lasting artifact such as a dataset, benchmark, evaluation, or tool, supported by a mentor and technical write-up.

Leads revenue operations strategy, GTM systems, top-of-funnel experimentation, AI and automation deployment, data vendor relationships, forecasting, and pipeline optimization. Owns marketing and outbound automation, including API integrations, n8n workflows, scoring pipelines, reporting, monitoring, debugging, and enablement. Partners across Marketing, Sales, and Customer Success to scale revenue processes, validate growth hypotheses, and improve conversion systems.

Architect and deploy scalable machine learning and generative AI systems, including LLM, RAG, inference, evaluation, and LLMOps pipelines. Develop prompting, retrieval workflows, guardrails, schema validation, and production evaluation systems focused on accuracy, safety, reliability, and cost. Lead technical decisions, collaborate with product and engineering teams, drive greenfield initiatives, and mentor engineers building secure AI-powered experiences.

Design, build, and maintain scalable data platforms, architectures, and pipelines using Azure Fabric, Databricks, and Snowflake. Develop batch and real-time ingestion frameworks, optimize data workflows, perform ETL/ELT and data modeling, and maintain production-grade transformation layers. Ensure data security, compliance, reliability, and performance while collaborating with analysts, scientists, engineers, and nontechnical stakeholders to deliver effective data solutions.

Design, build, and maintain scalable data platforms, architectures, and pipelines using Azure Fabric, Databricks, and Snowflake. Optimize data workflows for reliability, scalability, performance, security, and compliance. Perform data modeling and ETL/ELT, improve data quality, and collaborate with analysts, scientists, engineers, and stakeholders to deliver tailored data solutions.

Design, build, and operate Rust services powering Scribd’s large-scale Content Library and core infrastructure. Own features end to end, evolve Python tooling, optimize DataFusion, Parquet, S3, caching, and PostgreSQL/Aurora systems, and strengthen governance through lineage, authorization, and deletion controls. Lead cross-functional technical projects, support platform users, mentor engineers, improve developer workflows, and maintain production reliability through observability, on-call, and incident response.

Designs and maintains scalable test automation frameworks using Java, Python, and Playwright. Performs functional, API, integration, database, migration, performance, security, and accessibility testing. Validates Snowflake data, ETL processes, transactions, and business rules with complex SQL. Integrates tests into CI/CD pipelines and uses generative AI tools to improve testing productivity and coverage. Establishes AI testing governance, analyzes defects, mentors junior engineers, and drives quality engineering improvements.

Provides technical support, troubleshooting, maintenance, and development for applications, databases, ETL workflows, APIs, and internal websites. Responsibilities include database optimization, monitoring, preventive maintenance, QA, user support, documentation, reporting, data integration, JavaScript automation, and DevOps improvements. Collaborates with stakeholders and developers to resolve issues, enhance reliability, prevent fraud, and automate processes.

Conduct advanced computer vision research in 2D/3D scene understanding, tracking, reconstruction, sensor fusion, and real-time perception. Integrate vision and language models with LLMs, optimize pipelines for embedded hardware, and develop evaluation frameworks for algorithmic and human-centered outcomes. Collaborate with engineering, hardware, and design teams to convert research into product-ready solutions and interactive prototypes.

Own and operate infrastructure for a large-scale sports betting and media platform across cloud and production environments. Lead infrastructure migrations, build Kubernetes platform tooling and CI/CD automation, improve observability and incident response, support development teams, troubleshoot distributed systems, reduce operational toil, and mentor engineers.

Lead implementation of complex digital blocks from synthesis through physical verification on advanced nodes (3nm). Drive timing closure, power and EM/IR analysis, full-chip floorplanning, IO and clock distribution, DRC/LVS cleanup, ECOs, and collaborate with EDA vendors. Automate and customize flows using Tcl/Python/Shell; support tape-out and photonics integration.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Develop, train, deploy, and optimize machine learning models for research and production ultrasound applications. Build scalable ML data pipelines and tools, automate retraining, evaluation, and monitoring, and improve inference speed, robustness, and maintainability. Collaborate with researchers and software engineers to productionize ML research while reducing technical debt and communicating concepts to technical audiences.

Leads a customer success engineering team while directly supporting enterprise customers. Responsibilities include technical onboarding, training, adoption, troubleshooting, success planning, QBRs, executive and technical workshops, renewals, expansion, and customer advocacy. The role serves as a technical expert on Cyera’s data security platform, mentors team members, manages escalations, collaborates with development teams, and drives customer satisfaction, retention, and return on investment.

Designs, secures, operates, and supports multi-account cloud infrastructure across AWS, Azure, and GCP. Responsibilities include AWS networking, Terraform infrastructure as code, GitHub Actions CI/CD pipelines, identity and access management, cloud governance, observability, incident response, disaster recovery, cost optimization, and compliance controls. The role also develops automation, maintains technical documentation, and provides after-hours support when needed.

Develops tester control and automation software for RAM testing hardware, supports Linux-based AI servers, and contributes to low-level memory stability and other R&D projects. The engineer will use C++, C#, and Python daily, collaborate with international R&D teams across time zones, and learn specialized hardware-testing technology.