Who this seat is for
This is not a generic SaaS QA role. We are building release confidence for an agentic control plane that runs across cloud, edge, and physical device integrations.
You are a QA-first engineer who has moved past manual testing into automation and operational tooling. You treat quality as a property of the product, not a phase at the end of a sprint.
You have lived inside CI/CD pipelines. You understand cloud deployment. You can reason about whether a release is safe to ship. You are comfortable in a startup where the person who writes the test plan also adjusts the pipeline that runs it.
Our engineering work is AI-assisted day to day, and the platform itself runs on AI. We expect you to already use — or be ready to adopt — tools like Cursor, GitHub Copilot, or Claude Code in your QA automation and DevOps work. Hands-on experience designing test or evaluation workflows for AI systems is a strong signal.
What you will own
- Manual and automated test plan design and execution
- Release validation across cloud, edge, and device integration workflows
- Regression risk identification and platform stability monitoring
- CI/CD pipeline maintenance and improvements
- Environment management across Dev, Beta, and Production
- Deployment quality and operational health monitoring
- Observability and logging contributions
- Infrastructure automation and operational tooling
You will work directly with the backend, frontend, and AI engineers inside sprint cycles. With product engineering on release readiness. With the EVP Product and Engineering on the discipline standards that keep us shippable.
Hard requirements
- Direct experience in software QA and modern testing workflows including automation
- Working exposure to DevOps practices and CI/CD systems
- Familiarity with cloud platforms (AWS preferred) and deployment pipelines
- API testing and debugging experience
- Strong troubleshooting and analytical chops
- Daily comfort with AI-assisted engineering tools (Cursor, GitHub Copilot, Claude Code, or similar) in QA and DevOps workflows
High-signal indicators
- GitHub Actions, Azure DevOps, or comparable CI/CD tooling experience
- Observability and logging system exposure (Elastic, Datadog, or similar)
- Docker and containerized environment familiarity
- Designed test or evaluation workflows for AI systems — LLM output validation, RAG pipeline tests, prompt-based test orchestration, or similar
- Startup or SaaS environment where QA also touched operations
What this role means here
You will own release validation across cloud, edge, and device integrations. You will reduce regression and operational issues through systematic test coverage. You will raise operational discipline without slowing delivery. You will partner with engineering inside sprints, not at the end of them. You will improve deployment confidence so the team ships faster, not slower.
You are accountable for the answer to "is this release safe to ship" — and you have the tooling to back the answer up.
Not a fit if
- Your experience is purely manual QA with no automation framework exposure
- You have not worked inside a CI/CD pipeline or cloud deployment process
- You have no experience with API testing or debugging distributed systems
- You prefer environments that do not use AI-assisted tooling
Why this seat matters
The agentic control plane sits between AI reasoning, orchestration logic, and physical device endpoints. A bad release can break a customer environment. Release confidence is a competitive advantage.
The person in this seat defines the operational discipline that lets us ship faster as the platform grows, builds the testing and observability foundation that lets AI work be trusted in production, and strengthens the bridge between engineering velocity and enterprise reliability.