Top Python Jobs in Toronto

Own the end-to-end architecture of a governed, zero-copy, cross-cloud data platform spanning Databricks, Snowflake, and other data sources. Define architecture standards, reference designs, metadata and catalog integrations, data contracts, access controls, and agent- or MCP-based data consumption patterns. Translate product vision into technical specifications, guide engineering decisions, ensure enterprise alignment, and communicate complex trade-offs to technical and business stakeholders in a consulting environment.

Develop and maintain Triton compiler front-end and back-end components for Intel GPUs. Design and implement optimization passes, code generation, testing, debugging, and releases. Collaborate with hardware teams, compiler partners, and programming communities; participate in language/standards groups.

Lead technical analysis and design for data solutions, create data models and mapping documents, identify input/output data, mentor junior analysts, and support data warehousing, ETL, and cloud-based implementations.

Build and deploy production-ready AI applications, including LLM integrations, RAG pipelines, agentic workflows, and automated triggers. Partner with strategy, sales, and operations teams to identify business opportunities, integrate AI systems with Snowflake, APIs, and third-party tools, optimize manual processes, and implement evaluation and monitoring frameworks. Communicate technical solutions to non-technical stakeholders while owning projects from discovery through deployment.

Conducts geospatial data collection, processing, analysis, modeling, automation, and visualization for energy and environmental projects. Develops GIS tools and services using ESRI applications, Python, ArcPy, ModelBuilder, and JupyterLab. Produces maps, reports, and analytical inputs for renewable energy assessments, site selection, environmental studies, and energy equity evaluations. Collaborates with multidisciplinary teams and supports digital transformation. The role is based in a hybrid DNV office setting and includes occasional site visits and travel up to 5%.

Build infrastructure and software for training, deploying, scaling, monitoring, and operating machine-learning models and optimization algorithms for supply-chain applications. Responsibilities include translating supply-chain use cases into mathematical models, developing ML and optimization systems in C++ and Python, designing automated testing strategies, and supporting cloud or containerized workloads. The role also involves research into heuristics, optimization, GPU computing, MLOps, and AI-assisted software development.

Lead applied AI and machine learning innovation for supply chain products. Develop prototypes and proof-of-concepts, evaluate emerging techniques, and guide promising ideas toward scalable enterprise capabilities. Provide technical leadership across research, product, and engineering teams while remaining hands-on with ML, generative AI, agents, LLMs, optimization, recommendation, and explainability. Influence architecture and technical direction, mentor colleagues, assess AI-generated code, and ensure solutions meet quality, security, maintainability, and customer-impact standards.

Develop and optimize supply-chain algorithms by translating real-world problems into mathematical models. Lead C++ implementation, investigate heuristics and optimization techniques, and create comprehensive functional, regression, performance, unit, and integration tests. Collaborate with agile teams and stakeholders while improving software quality, development speed, and platform capabilities. Experience with commercial mathematical solvers, data structures, algorithms, and AI-assisted development is valued.

Designs and operates scalable AWS infrastructure, builds containerized systems with Docker and Kubernetes, manages Terraform infrastructure as code, improves Jenkins and GitHub Actions CI/CD pipelines, and implements monitoring, logging, troubleshooting, and root cause analysis. The role partners with engineering teams to improve reliability, security, performance, and deployment workflows in complex SaaS environments.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Build and maintain scalable full-stack web application features and distributed microservices. Own complex features from technical design through production deployment, improve web application performance and reliability, participate in on-call support, troubleshoot incidents, and enhance CI/CD, containerization, and observability. Collaborate with product, infrastructure, design, and API teams while mentoring engineers and contributing to technical strategy.

Designs and operates scalable ML infrastructure, including training, serving, inference, orchestration, and deployment pipelines. Optimizes GPU and CPU workloads, manages cloud clusters, implements observability and SLOs, and secures automated CI/CD and infrastructure-as-code workflows. Partners with research and AI engineering teams to productionize models, while building reusable tooling, templates, and runbooks. The role also supports low-latency model serving, data quality, model promotion, and cost optimization.

Provide Tier 2 production support and reliability engineering for enterprise transactional platforms. Responsibilities include incident triage, server/database/cloud administration, automation (scripting, IaC), CI/CD and release support, observability, security hygiene, PostgreSQL/RDBMS operational tasks, root cause analysis, and 24x7 on-call and on-site support for deployments and DR activities.

Develop and optimize large-scale advertising ranking models for CTR and CVR prediction, calibration, user representations, and sequence modeling. Own machine learning systems end to end, including data pipelines, feature engineering, training, evaluation, deployment, and maintenance. Collaborate with platform, product, data science, and engineering teams to run experiments and improve advertiser outcomes, revenue, and user relevance. Apply deep learning and recommendation techniques within production latency, reliability, and cost constraints.

The role involves leading financial analysis for technology spend, designing data models, developing visualizations, and ensuring data quality, while collaborating with internal stakeholders across the company.

Serve as primary customer contact for strategic accounts, driving onboarding, technical integrations, and analyses. Provide product education, conduct API and timeseries analyses, develop BI tools, research incentives and regulations, and feedback to Product and Engineering. Support Sales and Marketing with battery storage domain expertise to maximize customer value and retention.

Build and operate production systems for Juniper Square’s AI Platform, including APIs, infrastructure, reusable frameworks, and AI/LLM capabilities. Own projects end to end, ensure reliability and observability, contribute to technical design, and support incident response. Collaborate with product, design, QA, and engineering teams while applying AI-native development practices. The role requires strong Python backend engineering, cloud-native technologies, databases, production operations, and hands-on experience with RAG, agents, evaluation workflows, or related AI systems.

Designs and implements Okta identity solutions for enterprise customers. Leads requirements gathering, discovery workshops, technical architecture, functional and system specifications, solution design, and implementation oversight. Acts as a trusted customer advisor, translates business needs into actionable strategies, supports project planning, conducts health checks, and communicates technical information to executives and nontechnical stakeholders. Requires extensive IT architecture, consulting, IAM, cloud, application integration, and customer identity experience, with travel up to 50%.

Designs and leads customer infrastructure solutions within professional services. Responsibilities include architectural planning, deployment documentation, deployment reviews, mentoring engineers, customer and project manager collaboration, troubleshooting, technical reviews, and maintaining vendor certifications. The role requires extensive experience with Dell EMC storage, virtualization, hyper-converged systems, networking, backup technologies, data centers, technical documentation, and onsite hardware installation. Travel up to 45% and after-hours support are required.

Conduct machine learning research operations using existing infrastructure. Evaluate neural network quality, failure modes, and edge cases; coordinate experiments with researchers; manage concurrent projects and deadlines; review results; recommend theoretical and process improvements; and prepare technical reports communicating qualitative and quantitative findings to external audiences.

Research Engineer developing and extending algorithms for large-scale unsupervised learning and perception problems. Responsibilities include maintaining deep learning experimentation tools, researching novel AI approaches, and deploying algorithms to internal and customer autonomous vehicle platforms. The role requires collaboration with software and autonomous vehicle engineers, rapid experimentation, and practical delivery of customer projects.

Build and scale AI-agent infrastructure, including orchestration, tool calling, context management, guardrails, evaluations, and LLM-as-judge pipelines. Develop Python and Node.js microservices, Laravel APIs, AWS infrastructure, and optimized MySQL systems. Diagnose agent failures, improve prompts and tools, maintain reliability and observability, contribute to architecture, review code, collaborate cross-functionally, and mentor engineers.

Designs and governs the enterprise Google Workspace and Gemini AI ecosystem. Responsibilities include tenant architecture, AI rollout and governance, security policies, DLP, access controls, compliance, eDiscovery, automation, operational playbooks, Tier 4 escalation, and ITIL integration. The role mentors administrators, evaluates product roadmaps, leads cross-functional security initiatives, and ensures reliable, secure, and compliant cloud collaboration workflows.

Designs, analyzes, and interprets real-world evidence studies using EMR and claims data. Develops protocols, data specifications, statistical analysis plans, cohort definitions, causal inference methods, trial emulations, external control arms, and sample size calculations. Collaborates with HEOR, market access, regulatory, and clinical development teams, translating findings into reports and presentations. Requires strong RWE, observational study, healthcare coding, statistical programming, SQL, OMOP, and RWE CMH expertise.

Build and operate secure, scalable agentic AI applications and workflows across Azure and AWS. Lead initiatives from discovery and architecture through production delivery, rollout, adoption, and operational handoff. Develop AI agents, APIs, integrations, RAG solutions, and reusable platform services; evaluate emerging technologies; establish governance, observability, and responsible AI practices; and coach teams on secure AI adoption. Partner with business, engineering, security, governance, and risk stakeholders to deliver measurable value.