Top Python Jobs in Toronto

Design and implement end-to-end AI systems and production-grade components. Build and maintain RAG pipelines, backend services, APIs, and data pipelines. Write Python code using Azure AI SDK. Implement document digitization, vectorization, semantic search, and content summarization. Prepare training/evaluation datasets, participate in code reviews, and drive engineering quality and deployment of AI-powered SaaS workflows.

Build and scale a live ML data streaming analytics platform, lead greenfield real-time processing projects (Python/Rust), and design reliable backend services using cloud and orchestration tools.

Build and maintain scalable SaaS platform integrations connecting CoLab to CAD, PLM, ERP and enterprise systems. Design, develop, test, and ship user-facing features and robust APIs. Participate in code reviews, mentor junior engineers, contribute to technical direction, and improve product UX and performance.

Lead quality strategy and testing for LLM-powered and agentic systems. Design evaluation frameworks, build scalable automated test suites, define quality metrics, integrate tests into CI/CD, mentor QE org, and partner with cross-functional teams to ensure release readiness for AI products.

Partner with web product, marketing, engineering, and finance teams to own web analytics strategy and drive actionable decisions. Design and evaluate experiments and causal analyses, define metrics, ensure event instrumentation, and translate findings into product and growth recommendations. Use SQL and Python/R for statistical analysis, deploy experimentation platforms, and help shape AI-native analytics automation. Focus is on measurement, causality, and moving teams to act—not production ML.

Lead and resolve high-severity post-sales technical escalations through deep-dive investigation, reproduction, and engineering handoff. Own customer calls, collect logs/evidence, file bug reports, monitor patterns, and improve tooling and runbooks in partnership with Support and Engineering.

Lead end-to-end AI product development: design, build, deploy, and iterate AI-native features (agents, RAG, tool-use). Own full stack (frontend, backend, data, model integration), establish feedback loops (evals, telemetry), partner with product and operations, and make pragmatic build-vs-buy decisions to deliver production-quality AI features for restaurant operations.

Intern will help design and implement the openJiuwen AI Agent Platform and enterprise data foundations: knowledge graphs, vector DBs, workflows, agent frameworks, tool-calling, and multi-agent systems. Contribute to open-source projects (JiuwenSwarm), build real-world AI agents, explore LLM, RAG, memory, planning, and evaluation technologies, and support documentation and community collaboration.

The Data Engineer is responsible for designing and optimizing data pipelines, ensuring data integration, and collaborating with teams on AI-driven applications.

Design, develop, and deploy scalable machine learning solutions, integrate ML with data warehouses, and optimize model performance in production environments.

Lead a small data engineering team to deliver trusted marketing data pipelines and integrations, define success metrics, ensure data quality, enable marketing platforms, and align data roadmap with business growth.

Lead design and delivery of AI agent solutions that convert documents into structured data for Veeva Quality Cloud. Define data flows, orchestration, model integration, and prompts; collaborate with engineering and UX to automate processes, evaluate AI agents, and present features to stakeholders.

Lead design and delivery of AI Agent solutions for Veeva QualityDocs and Training. Define data flows, orchestration, prompts, and model integration; collaborate with engineering and UX; evaluate AI agents and present capabilities to stakeholders.

Senior SDET responsible for automation-first quality engineering for AI-driven CX platform. Build and maintain fast, parallelized regression suites, load/stress tests, CI-integrated tests, API/contract tests, and synthetic traffic generation. Rapidly detect, isolate, and report issues; collaborate with devs to ensure weekly deploy quality for real-time, event-driven services.

Design, build, and operate ML data pipelines and unsupervised models to detect anomalous patterns in healthcare claims. Develop feature engineering, batch processing, and REST API model serving (FastAPI). Integrate with Oracle via SQLAlchemy/oracledb, maintain CI/CD and on-prem deployment workflows, collaborate with business teams to interpret clusters, and support model validation, operationalization, and audit artifacts.

Lead technical triage and resolution for full‑stack production issues, build internal tools and automations, expand observability, harden CI/CD and deployments, and partner with implementation and product teams to streamline customer rollouts and influence product roadmap around AI, cloud, and platform reliability.

Lead analytics strategy for C2C M&A integrations, driving product analytics, experimentation, metric standards, AI-enabled prototypes, and cross-functional data solutions to improve seller outcomes and business performance.

Design, build, and operate AI-driven detection engineering systems and orchestrated agent workflows to automatically generate, validate, optimize, and deploy cybersecurity detections. Focus on token-efficient LLM usage, deterministic pipelines (Python), CI/CD integration, context engineering, and embedding AI into SOC detection lifecycles.

Build modular, reusable AppOps frameworks, tools, and libraries to embed logging, monitoring, alerting, and observability into application code and CI/CD. Automate deployments, testing, recovery, and operational checks; maintain version-controlled templates and documentation. Collaborate with engineering, operations, and security teams to improve post-deployment performance and reduce manual intervention.

Lead design and implementation of application systems using Agile and DevOps practices. Drive microservices, CI/CD, observability, and cloud/on-prem integrations. Mentor teams, build POCs, troubleshoot production issues, and ensure compliance and operational readiness while partnering with business stakeholders.

Lead strategic credit risk initiatives to improve adjudication quality, data integrity, and oversight using analytics and AI tools. Partner with senior leaders, analytics teams, and business stakeholders to design automated controls, inform decision frameworks, and deliver executive-level recommendations that strengthen portfolio risk management.

Founding technical role to design, build, and deploy internal AI tools and agents using LLMs and retrieval systems. Partner with investors and the data team to accelerate sourcing, diligence, portfolio insights, and define CRV's AI strategy through rapid prototyping and production-grade systems.

Lead the buildout of a centralized marketing analytics function: consolidate multi-unit data, define KPIs and governance, develop pipelines and dashboards, improve attribution, create forecasting and testing frameworks, and deliver insights to optimize acquisition and marketing ROI.

As a Senior AI Full Stack Developer, you will build and deploy AI-powered tools, integrate APIs, optimize workflows, and collaborate across teams to enhance cybersecurity practices.

Drive AI-enabled ASIC development methodology by identifying cross-stage bottlenecks, building data pipelines and metrics, creating automation and AI models, mentoring engineers, and evaluating advances in AI and EDA to improve predictability, convergence, and cycle time.