SDET Architect

Carbon60•Toronto, ON

6d•CA$160,000 - CA$180,000•Hybrid

About The Position

This position is for a Principal Software Development Engineer in Test who embeds directly with customers as a hands-on quality partner while also building production features. The role blends test engineering with full-stack development: you'll help build the systems and the safeguards that prove they work. You'll apply agentic development and AI-assisted workflows, using behavior-oriented prompts and specifications to guide AI agents to generate both implementation and verification. A core component of this role involves working directly with customers to modernize their technology estates and raise their quality bar, with the goal of supporting business growth. This is a hands-on technical role focused on building, verifying, and improving engineering quality.

Requirements

5-7 Years of Experience: Proven experience as a Senior SDET, Test Architect, Test Engineer, or Software Engineer delivering distributed systems with a strong quality focus.
Customer-Facing Aptitude: Ability to work directly with customers and stakeholders — communicating clearly with both technical and non-technical audiences, managing expectations, and building trust.
Test Strategy & Automation: Strong experience designing test frameworks and automation across the stack — unit, integration, contract, E2E, performance, and load.
Hands-on with tools such as Playwright, Cypress, Selenium, Jest/Vitest, pytest, JUnit, and k6.
Full-Stack Development: Solid ability to build production features, not just test them.
Frontend: Proficiency with modern JavaScript/TypeScript frameworks (e.g., React, Next.js, Vue, Svelte) and their testing ecosystems.
Backend: Experience building and testing APIs (REST, GraphQL, gRPC) and server-side architecture using languages such as Node.js, Go, or Python.
Polyglot Proficiency: Proficiency in at least two distinct programming languages (e.g., TypeScript, Python, Go, .NET, Java).
Generative AI & Agentic Development Experience: Experience using agentic development tools and workflows where AI agents drive code and test generation.
Ability to write effective prompts and specifications and manage the lifecycle of AI-generated artifacts, including verifying their correctness.
CI/CD & Quality Gates: Experience with CI/CD pipelines (GitHub Actions, GitLab CI, or similar), quality gates, and test orchestration in automated delivery flows.
Observability & Reliability: Familiarity with monitoring, tracing, SLOs, and resilience/chaos testing to validate systems under real-world conditions.
Business-Aligned Engineering: Ability to align quality strategy and feature development with customer business goals.
Rapid Skill Acquisition: Ability to acquire proficiency in unknown codebases or new languages.
System Design: Familiarity with microservices, event-driven architecture, cloud-native patterns (Kubernetes, Docker), and databases (SQL & NoSQL).
Cloud Providers: AWS or Azure.
Willingness to Travel: Comfort with occasional travel to customer sites as needed.

Nice To Haves

Prior experience in a Forward Deployed Engineer, SDET, Test Architect, Test Engineer, or consulting/professional services role.
Experience building quality practices and test infrastructure inside customer teams.
Experience with non-functional testing: security, accessibility, and performance profiling.
Experience transforming brownfield applications into modern, testable architectures.
Experience migrating and modernizing applications from on-premise to the Cloud.

Responsibilities

Work directly in customer environments to understand their business, workflows, and technical constraints firsthand.
Contribute to the end-to-end delivery of solutions — from scoping through implementation, verification, and deployment — building trust as a reliable partner for both features and quality.
Bring clarity to evolving problem spaces.
Apply an agent-first methodology using Generative AI.
Create structured, behavior-oriented prompts and specifications that guide AI agents to generate implementation code and the tests that verify it.
Confirm that AI-generated code actually conforms to intent — closing the gap between what was asked for and what was built.
Design and build test frameworks and automation across the stack — unit, integration, contract, end-to-end, performance, and load testing.
Help establish quality gates, test orchestration, and coverage standards that scale across polyglot codebases and customer teams.
Build production-grade features alongside your quality work.
Contribute to scalable, resilient systems and translate business requirements into technical specifications that AI agents and engineering teams use as the foundation for development.
Integrate AI tools like Claude Code, Kiro and Cursor into development and testing workflows.
Evolve your engineering practice from manual coding and test-writing to auditing, validating, and refining AI-generated solutions.
Apply the judgment to catch coverage theater — AI-generated tests that pass but prove nothing.
Follow repeatable AI-driven development patterns.
Build with testability, resilience, and observability in mind from the outset.
Participate in implementation reviews to ensure alignment with patterns, performance, and security expectations.
Write and test production-grade code across multiple languages and paradigms (e.g., TypeScript, Python, Go, .NET, Java) based on specific problem domains.
Ramp into unfamiliar stacks or codebases.
Help raise the engineering and quality bar within customer teams through pairing, code reviews, and mentoring.
Support customer engineers in adopting agentic development, AI-assisted workflows, shift-left testing, and modern architectural practices so solutions remain sustainable long-term.