Senior Automation QA (JavaScript) Engineer

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.

• 8+ years of experience in test automation, API testing, and building reusable test frameworks adopted by other engineers.
• Strong proficiency in Playwright and TypeScript is mandatory.
• Extensive experience developing Playwright/TypeScript-based automation frameworks and integrating them into secure CI/CD environments.
• Hands-on experience testing LLM-powered or other non-deterministic systems, including statistical assertions, semantic scoring, and model-variance management.
• Deep understanding of Large Language Model behavior, prompt sensitivity, and common RAG failure modes.
• Strong experience testing asynchronous and event-driven systems, including failure injection, idempotency verification, and eventual-consistency assertions.
• Experience with Temporal or a similar workflow orchestration engine is highly desirable.
• Strong CI/CD expertise, particularly with GitHub Actions or equivalent platforms, and experience establishing automated release gates.
• Solid SQL and data-validation skills, including the ability to build synthetic test-data pipelines and assess dataset quality.
• Strong analytical skills and experience interpreting evaluation scores to provide actionable feedback to AI engineers and support prompt tuning.
• Security-focused and adversarial mindset, with familiarity with prompt-injection techniques or strong application security testing experience.
• Ability to determine appropriate testing depth under delivery and milestone pressure and confidently communicate quality evidence to technical and non-technical stakeholders.
• Strong understanding of automation strategy, software quality principles, debugging, and testability.
• Ability to work collaboratively with AI engineers, platform engineers, product teams, and evaluation specialists.
• Strong communication, mentoring, and knowledge-sharing skills.
• Experience working in fast-paced, cross-functional engineering environments.

Preferred qualifications include:

• Experience with performance and load-testing tools such as k6 or Locust.
• Experience testing multi-tenant SaaS isolation and security boundaries.
• Familiarity with AI evaluation and observability platforms such as Langfuse, LangSmith, Promptfoo, DeepEval, or Arize Phoenix.
• Experience producing compliance evidence for frameworks such as SOC 2 or GDPR.
• Experience with accessibility testing, including Section 508 or WCAG.
• Familiarity with Commercial Real Estate workflows, lease accounting, CAM reconciliation, or document-extraction systems.
• Experience using LLMs to generate test cases, adversarial payloads, or synthetic documents.
• Experience testing high-concurrency background processing within agentic systems.
• Experience with qTest or similar test-management platforms.

Benefits:

• Remote work opportunity based in India.
• Opportunity to work on an enterprise-grade AI and Agent Development Platform.
• High-impact role where automated testing directly influences release gates and client milestone decisions.
• Exposure to advanced AI/LLM testing, agent evaluation, distributed workflows, security testing, and observability.
• Opportunity to work with technologies such as Playwright, TypeScript, Temporal, Langfuse, OpenTelemetry, CI/CD platforms, and modern cloud infrastructure.
• Strong professional community and collaborative, open-door working environment.
• Access to internal meetups, conferences, workshops, Udemy, language courses, and company-paid certifications.
• Opportunities for internal mobility and exposure to diverse technology domains and large-scale projects.
• Company-paid medical insurance.
• Mental health support.
• Financial and legal consultation support.
• Opportunity to collaborate with global engineering teams and work on technology with international impact.