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Role: Expert Automation QA JavaScript Engineer Experience: 11+ Years Location: Remote Employment Type: Haparz Payroll About the Role We are looking for an Expert Automation QA JavaScript Engineer to drive quality engineering for enterprise-scale AI platforms and agentic systems. You will build automation frameworks, validate non-deterministic AI behavior, establish release quality gates, and ensure reliability, observability, and performance across modern distributed applications. Key Responsibilities Design, build, and maintain scalable automation frameworks using JavaScript/TypeScript that are adopted across engineering teams. Automate API, integration, platform, and end-to-end testing for AI-powered applications. Validate platform non-functional requirements including invocation latency (P95), concurrency, queue backpressure, and LLM provider failover. Develop automated validation for asynchronous and event-driven workflows, including failure injection, retry handling, idempotency, and eventual consistency. Build intelligent validation approaches for LLM-based applications using semantic scoring, statistical assertions, and model variance analysis. Leverage Langfuse and OpenTelemetry traces to validate trace completeness, token usage, cost accounting, and anomaly detection. Integrate automated quality gates into CI/CD pipelines with regression detection for every release. Produce auditable test evidence and quality reports supporting customer milestone sign-offs. Collaborate with AI architects and platform engineers to define acceptance criteria, observability, and testability during solution design. Maintain mock LLM services, synthetic datasets, and deterministic testing environments for fast and reliable execution. Mentor QA engineers and contribute reusable AI testing frameworks, automation standards, and engineering best practices. Required Experience 11+ years of experience in Automation Testing with JavaScript/TypeScript. Expertise in API automation and enterprise automation framework development. Proven experience testing AI/LLM-powered or other non-deterministic applications. Strong understanding of semantic validation, statistical testing, and managing AI model variability. Experience testing distributed, asynchronous, event-driven systems. Hands-on knowledge of failure injection, resilience testing, eventual consistency, and idempotency validation. Experience with CI/CD pipelines such as GitHub Actions or equivalent and implementing automated release gates. Strong SQL, debugging, and performance analysis skills. Experience with OpenTelemetry, Langfuse, or similar observability platforms is highly desirable. Experience with Temporal or similar workflow orchestration platforms is an advantage. Work Location: Remote .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.