Sr Automation QA (JS) Engineer

Job Summary


Responsibilities

• Design, build, and maintain automated test suites for the platform, Frontend and Backend, including contract tests that protect the agent-developer experience as the SDK evolves
• Build automated tests for long-running Temporal workflows including failure injection (worker kill, provider outage, retry storms) to verify durable-execution guarantees such as zero lost runs and correct compensation/saga behavior
• Automate multi-tenant isolation verification cross-tenant data access, configuration bleed, and cost-attribution correctness executed per release as a platform acceptance criterion
• Verify agent execution sandboxing and egress controls through negative testing of disallowed tool calls and network destinations
• Design test strategies for LLM-driven behavior: statistical assertions over repeated runs (pass^k consistency), semantic-similarity scoring, confidence-threshold validation, and flakiness quarantine that distinguishes model variance from genuine regression
• Build and continuously extend adversarial test corpora document-borne prompt injection in lease PDFs, tool-call hijack, and data-exfiltration probes aligned to OWASP LLM Top 10 and recognized red-team taxonomies
• Verify Human-in-the-Loop (HITL) escalation behavior confidence thresholds fire correctly and no gated/irreversible action completes without authorization
• Partner with the evaluation function on golden-dataset-driven checks, contributing test infrastructure, synthetic-data (synthetic lease) pipelines, and CI integration that make agent quality measurable and repeatable
• Automate verification of platform NFRs invocation overhead (p95), concurrency targets, queue backpressure behavior, and LLM provider failover time and surface trends per release
• Use tracing data (Langfuse / OpenTelemetry spans) as a first-class test asset: assert on trace completeness, token/cost-accounting accuracy, and alerting behavior for anomalous runs
• Wire test suites into CI/CD as hard release gates with automated, per-metric regression flagging on pull requests
• Produce auditable, reproducible test-evidence packs that feed directly into client milestone sign-off
• Embed with AI and platform engineers from the design stage making acceptance criteria, testability, and observability inputs to architecture, not afterthoughts
• Maintain test environments, mocked LLM/provider layers, and synthetic data generators that keep test runs fast, deterministic where possible, and affordable
• Mentor mid-level QA engineers, codify AI testing patterns into reusable internal frameworks, and contribute to Ciklums quality engineering practice and AI Academy

Requirements

• 8+ years of test automation with API testing, and building test frameworks other engineers adopt, not just test cases
• Hands-on experience testing LLM-powered or other non-deterministic systems statistical assertions, semantic scoring, and managing model variance (this is the differentiator; cite real examples)
• Experience testing asynchronous, event-driven systems failure injection, idempotency verification, eventual-consistency assertions; Temporal or a similar workflow engine a strong plus
• Strong CI/CD integration skills (GitHub Actions or equivalent) and the discipline to design release gates
• Solid SQL and data-validation skills; comfort building synthetic test-data pipelines and measuring dataset quality
• A security-testing and adversarial mindset; familiarity with prompt-injection techniques or a strong AppSec testing background with willingness to specialize fast
• Demonstrated judgment on what "tested enough" means under milestone pressure, and the ability to defend quality evidence to non-engineering stakeholders
• Strong proficiency in Playwright and TypeScript is mandatory for building automated test suites
• QA Automation Core: Extensive experience in building automated test frameworks (Playwright/Typescript-based) and integrating them into high-security CI/CD pipelines
• AI/LLM Literacy: Deep understanding of Large Language Model behaviors, prompt sensitivity, and the common failure modes of RAG (Retrieval-Augmented Generation) systems
• Analytical Mindset: Strong capability in statistical analysis to interpret evaluation scores and provide actionable feedback to AI Engineers for prompt tuning

Desirable

• Performance/load tooling (k6, Locust) for concurrency and latency NFR verification
• Prior testing of multi-tenant SaaS isolation
• Exposure to eval/observability stacks (Langfuse, LangSmith, Promptfoo, DeepEval, Arize Phoenix)
• Compliance evidence experience (SOC 2, GDPR) or accessibility testing (Section 508 / WCAG)
• Familiarity with Commercial Real Estate workflows (lease accounting, CAM reconciliation) or document-extraction systems (OCR, layout parsing)
• Experience using LLMs to generate test cases, adversarial payloads, or synthetic documents
• Performance Testing: Experience testing high-concurrency background processing within agentic systems
• Experience with qTest for test management

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.