QA Automation Engineer
Reference: ADGQAAutoEng_1787209506
QA Automation Engineer - AI & Agentic Systems
Level: Senior IC
Location: Remote
Role Summary
We are building agentic AI systems that can interpret complex data sources, documentation, and structured information to perform analysis, validation, and decision-support tasks. This role exists to make those agents trustworthy enough to act on.
This is a senior, hands-on quality role weighted toward testing non-deterministic AI and agentic systems. That is where most of your time sits and where the hiring bar is highest. You will also own the broader quality surface: integration, API, and performance testing are part of the remit, not out of scope.
The differentiator for this role is the ability to define what "good" looks like when a system reasons, calls tools, and can be wrong in subtle ways. You will scope, build, and run the frameworks yourself, with the independence of a senior engineer, and decide what to build first.
Key Responsibilities
- Agentic System Testing (Primary Focus)
- Tool-Use & Trajectory Evaluation: Test whether agents select the right tools for the right reasons and follow sound multi-step trajectories, not just whether the final answer looks plausible. Evaluate planning, intermediate steps, and recovery when a tool fails or returns nothing.
- Grounding & Citation Verification: Verify that agent claims are backed by cited evidence in source data, documents, or knowledge bases, and that referenced locations actually support the answer, catching confident but unsupported outputs.
- Honest-Failure & Refusal Calibration: Assert that agents ask for clarification or respond appropriately when information cannot be confidently determined, rather than inventing answers.
- Guardrail & Adversarial Testing: Probe prompt injection, jailbreaks, and instructions hidden within ingested content, ensuring the agent treats source content as untrusted data.
- Multi-Turn & State: Validate follow-ups, references to prior turns, and that conversational state carries correctly across a session.
2. AI & LLM Validation
- Non-Deterministic Testing: Architect automated frameworks that score generative AI outputs for hallucination, consistency, and factual accuracy against gold-standard datasets using LLM-as-judge methods calibrated against human judgement.
- Prompt & Model Regression: Design regression suites that catch prompt drift and model-version drift, ensuring changes to models or system instructions do not quietly degrade quality. Own the ground-truth and evaluation datasets these depend on.
- Live Production Quality
- Continuous Evaluation: Extend evaluation beyond pre-release into production, continuously scoring live agent outputs so quality is measured on real usage, not only in test environments.
- Monitoring & Alerting: Build quality monitoring that flags regressions, drift, and anomalous agent behaviour before users discover them.
- Quality Incident Response: Triage quality incidents and trace failures back to specific model versions, prompts, or datasets, feeding fixes into the development cycle.
- Integration, API & Performance
- Backend, UI & API Testing: Build robust integration tests that validate API integration across services and key user-facing flows.
- Secure Gateway Validation: Automate testing of secure API gateways, verifying that Role-Based Access Control (RBAC) and PII-redaction logic work correctly before data reaches AI models.
- Performance & Load: Own performance test plans and implementation (for example, Locust, JMeter, k6), validating latency, throughput, and stability under realistic load.
- Data, Traceability & Quality Gates
- Data Validation: Use SQL and data-validation tooling (for example, Great Expectations) to verify data quality across data platforms and vector databases, including the ground-truth and retrieval corpora that agents depend on.
- Requirements Traceability: Map test and evaluation cases to system requirements and user needs, producing verification-and-validation evidence and quality reports needed to ship with confidence.
- Quality Gates: Enforce quality gates in GitLab CI/CD that prevent non-compliant models or code from merging and prepare readiness evidence for release reviews.
Technical Requirements
- AI Evaluation (Core): Hands-on experience with LLM/agent evaluation frameworks (e.g., DeepEval, TruLens, RAGAS, or custom Python evaluators) and LLM-as-judge techniques.
- Agent Observability: Experience tracing and debugging agent runs, including tool calls, intermediate steps, token usage, and latency, using tools such as LangSmith, Langfuse, or OpenTelemetry-based tracing.
- Core Automation: Expert Python for custom test harnesses and evaluation tooling (Pytest), plus standard automation libraries (Selenium/Playwright for UI, Requests for API).
- Performance Testing: Proven ability to design and implement performance test plans (e.g., Locust, JMeter, k6).
- Data Validation: Proficiency with SQL and data-validation tools, and familiarity with vector databases and retrieval corpora.
- CI/CD Integration: Integrating automated tests and evaluations into GitLab CI/CD pipelines and enforcing quality gates.
- Test Management & Reporting: Managing test reports and artifacts (e.g., TestRail, Allure) and communicating results clearly.
- Version Control & QE Practices: Maintaining code-based frameworks in Git/GitLab and applying modern quality-engineering practices.
- Traceability Tools: Familiarity with requirements-management tools (e.g., Jira, Linear, Jama, Polarion) and linking results to requirement IDs.
Professional Qualifications
- Experience: 5+ years in QA automation or quality engineering, with at least 2 years focused on testing ML models, LLM applications, or AI agents.
- Probabilistic Systems Judgement: Able to define pass/fail criteria for systems whose outputs are not identical every run and communicate confidence levels clearly to engineering leadership.
- Independent Operator: A senior individual contributor who scopes and builds testing and evaluation frameworks with minimal direction and prioritises what matters most.
- Collaboration: Works closely with engineering and product teams, understands existing systems quickly, and moves efficiently.
Nice to Have
- Domain Experience: Familiarity with data-intensive workflows, complex documentation, compliance-focused environments, or highly regulated industries is advantageous.
- Formal V&V Exposure: Exposure to structured systems-engineering governance, stage-gate reviews, or formal verification and validation practices is beneficial but not required. We value the discipline more than the certification.
GCS is acting as an Employment Business in relation to this vacancy.
QA Automation Engineer
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