Top Tech Jobs & Startup Jobs in Toronto

Own the company’s platform end to end across full-stack product development, Kafka and ClickHouse data/ML pipelines, cloud infrastructure, Kubernetes, Terraform, CI/CD, observability, security, and incident response. Build React and Node.js features, operate production systems, replace vendor-dependent infrastructure, and establish AI-assisted engineering workflows using tools such as Claude Code and Cursor. This is a hands-on technical leadership role for a senior individual contributor in a lean startup.

Own multi-cloud AWS and GCP infrastructure, Terraform automation, observability, CI/CD governance, security, secret management, and SOC 2 compliance automation. The role manages Kubernetes environments, logging and tracing pipelines, infrastructure migration, cost optimization, access controls, evidence collection, and audit-ready systems. It requires hands-on DevOps or SRE expertise, strong AWS and GCP knowledge, Terraform and GitHub Actions proficiency, and experience with modern data infrastructure and compliance frameworks.

Lead end-to-end development of scalable web applications supporting marketing analytics, identity resolution, and personalization. Build React and Node.js services on GCP, integrate MongoDB and third-party platforms, develop data visualizations, and implement testing, CI/CD, observability, and secure multi-tenant architectures. Use AI coding agents, prompt engineering, LLM integrations, and MCP servers to improve development workflows. Act as a feature lead responsible for architecture, technical specifications, documentation, and cross-functional communication.

Own end-to-end product quality through manual exploratory testing, automation, release validation, API and data verification, regression testing, and bug management. Build and maintain UI and API automation using Playwright or Cypress, integrate tests into CI/CD, validate data flows across application layers, and collaborate with engineering, product, design, business, and customer success teams in a fast-paced Agile environment.

Designs, deploys, and maintains production machine learning systems for identity resolution, scoring, deliverability, and personalization. Builds batch and real-time feature pipelines, develops classification models, integrates ML with graph databases, and manages monitoring, drift detection, retraining, and performance reporting. Partners with engineering to deliver low-latency APIs and scalable services while contributing to GPU-accelerated processing, MLOps standards, and large-scale graph computation.