QA / DevOps Engineer

QA / DevOps Engineer - NetSpeek

NetSpeek is the agentic control plane for enterprise physical infrastructure. We govern how AI agents reason about, decide on, and execute actions across enterprise endpoints. Our reasoning and execution layer - Lena - sits in customer production environments, where reliability, observability, and auditability are non-negotiable.

The QA / DevOps Engineer owns platform quality, release reliability, and the AI-assisted engineering workflows our team uses to ship safely.

What you'll work on

• Owning CI/CD pipelines that gate releases against the AI failure modes that matter (eval regressions, groundedness drift, performance regressions).
• Building observability around AI behavior in production - beyond standard infra metrics.
• Designing test strategies for workflows that interact with physical hardware (simulator-first, hardware-on-demand).
• Driving AI-assisted engineering practice: tooling, prompt review, and evaluation of AI code suggestions.

You're a fit if

• You have 3+ years of combined QA and DevOps experience.
• You've owned a CI/CD pipeline end-to-end in production.
• You have hands-on production experience with AWS (preferred), Azure, or GCP.
• You use AI-assisted engineering tools day-to-day (Cursor, GitHub Copilot, Claude Code, or similar).
• You're familiar with at least one observability stack (Elastic, Datadog, Splunk, Grafana, or similar).
• You have API testing experience (Postman, REST-assured, or custom tooling).

You probably aren't a fit if

• You see QA as separate from engineering rather than embedded in it.
• You haven't worked in environments where AI changes the failure modes.

How to apply

We prefer applications through our hiring site: https://build.netspeek.ai/roles/qa-devops-engineer

There are three paths there, and we prioritize them in this order:

1. Incident Lab / Case Study -work through a real scenario for this role and send your reasoning with the application. Read first. The scenario gives us the most signal on how you actually think.

2. Field Note -a single written take instead of the scenario. Read second. Faster than the lab, still a strong signal.

3. Standard application -resume and the basics through the site's apply form. Read third. Applying directly through Workable below also works, and we'll still read it. The site's three paths exist because they give us better context for the read.

Either path is read by a human on our hiring team. No AI scoring, no auto-rejection.

Read the Engineering Handbook and How We Evaluate before applying. After an offer

We run standard pre-employment checks before your start date: identity verification, right-to-work confirmation, employment verification, and (where lawful and role-relevant) a criminal record check. We don't run credit checks or online reputation scoring.

Requirements

Must-have

• 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; Azure or GCP also welcome) and deployment pipelines.
• API testing and debugging experience (Postman, REST-assured, or custom tooling).
• Strong troubleshooting and analytical skills.
• Daily comfort with AI-assisted engineering tools (Cursor, GitHub Copilot, Claude Code, or similar) in QA and DevOps workflows.

Strong signal

• GitHub Actions, Azure DevOps, or comparable CI/CD tooling experience.
• Observability and logging system exposure (Elastic, Datadog, Splunk, Grafana, or similar).
• Docker and containerized environment familiarity.
• Experience designing test or evaluation workflows for AI systems (LLM output validation, RAG pipeline testing, prompt-based test orchestration, or comparable).
• Startup or SaaS environment experience where QA also touched operations.

Benefits

We are growth-stage and fully remote, not late-stage. We invest in the work, the tools, and the people, not the manifesto.

What that looks like in practice:

• Flexible / unlimited time off
• Health insurance
• Equity participation, discussed at offer
• Fully remote
• Architectural ownership of work that ships to real enterprise customers
• Direct working relationships with the people setting platform strategy
• A growth-stage platform where the decisions you make in your first year shape the product for years
• AI-assisted tooling licensed by NetSpeek (Cursor, Claude Code, GitHub Copilot, or comparable)