This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Automation QA (JS) Engineer based in India.
As a Senior Automation QA Engineer, you will help establish the quality foundation for an enterprise-grade AI Agent Development Platform. You will design and own automation across frontend, backend, long-running workflows, multi-tenant infrastructure, security boundaries, and performance requirements. Because the platform relies on non-deterministic AI behavior, you will develop sophisticated testing approaches based on statistical assertions, semantic evaluation, and adversarial scenarios rather than simple expected-output checks. Your work will directly influence release readiness, client milestone sign-offs, and production reliability. You will collaborate closely with AI engineers, platform engineers, and evaluation specialists to embed quality and testability from the earliest design stages. This is a high-impact opportunity to shape reusable quality engineering practices for rapidly evolving AI-powered systems.
Accountabilities:
You will own the automation strategy across the platform, building reliable, reproducible, and scalable test infrastructure that provides defensible evidence of product quality and production readiness.
- Design, build, and maintain automated test suites covering frontend and backend functionality.
- Develop contract tests that protect the agent-developer experience as SDKs and platform capabilities evolve.
- Automate testing of long-running Temporal workflows, including worker failures, provider outages, retry storms, and other injected failures.
- Verify durable execution guarantees, including zero lost workflow runs, idempotency, recovery behavior, and correct compensation or saga execution.
- Automate multi-tenant isolation testing, including cross-tenant data access, configuration leakage, and cost-attribution correctness.
- Validate agent execution sandboxing and egress controls through negative testing of unauthorized tool calls and network destinations.
- Design testing strategies for LLM-driven and other non-deterministic behavior using statistical assertions, repeated-run consistency, semantic similarity scoring, and confidence thresholds.
- Identify and quarantine flaky tests while distinguishing expected model variance from genuine regressions.
- Build and maintain adversarial test corpora covering prompt injection, tool-call hijacking, data-exfiltration scenarios, and other AI security risks.
- Test Human-in-the-Loop escalation mechanisms to ensure confidence thresholds trigger correctly and gated or irreversible actions cannot proceed without authorization.
- Partner with evaluation teams on golden datasets, synthetic-data pipelines, and CI integrations that make AI quality measurable and repeatable.
- Automate verification of performance and reliability requirements, including p95 invocation overhead, concurrency targets, queue backpressure, and LLM provider failover.
- Use Langfuse, OpenTelemetry, and tracing data to validate trace completeness, token and cost accounting, and anomalous-run alerting.
- Integrate automated tests into CI/CD pipelines as hard release gates with per-metric regression detection.
- Produce auditable and reproducible test-evidence packages that support client milestone sign-offs.
- Collaborate with AI and platform engineers during system design to establish acceptance criteria, testability, and observability requirements.
- Maintain test environments, mocked LLM and provider layers, and synthetic data generators to keep testing efficient, deterministic where possible, and cost-effective.
- Mentor mid-level QA engineers and develop reusable AI testing frameworks, patterns, and best practices.
- Contribute to the broader quality engineering practice through knowledge sharing and technical enablement.
Requirements:
The ideal candidate is an experienced automation engineer with strong framework-building capabilities, advanced knowledge of AI/LLM testing, and the ability to assess complex systems from both reliability and adversarial perspectives.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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