Work Description :- Own the acceptance test plan: define how 95% detection accuracy and - 5% area variance are measured, and enforce the split between the calibration set used for tuning and the held-out set used for acceptance- Build and maintain the coverage matrix across design types and symbol classes, flagging thin or empty cells as known gaps- Report accuracy per symbol class with confidence intervals, not just an aggregate figure, and enforce the per-class minimum instance count below which a class is reported as provisional- Run no-regression checks across fine-tune rounds so a new model cannot pass on average while regressing on a rare but critical class- Build the Knowledge Bot evaluation suite: a curated Q&A; set with known-correct answers, scored for groundedness and faithfulness against every prompt and model version- Perform adversarial and AI security testing before production and after material architectural change prompt injection, jailbreak attempts, RAG-specific attacks, cross-user data isolation and AI data leakage- Validate correction-quality controls: spot-audit ambiguous classes, track inter-reviewer agreement,



and confirm conflicting human corrections are adjudicated before entering the labelled set- Stand up drift monitoring and produce the periodic model-health report covering drift indicators, correction rates, confidence-score trends, retraining events and acceptance results by version- Run functional and integration testing of the HE2 workflow alongside the model validation workWhat you need :- Python, and the ability to write your own evaluation harnesses rather than only driving a test tool- Solid grasp of ML evaluation: precision, recall, per-class metrics, confidence intervals, sampling bias, and why a held-out set must stay held out- Experience testing an ML or AI system in production - you have caught a model problem, not only an application bug- Test automation and CI, with pytest or equivalent- API testing and the discipline to build reproducible, versioned test datasets- Explicit written reporting: your reports go to the customer and support a payment gateNice to have :- LLM red-teaming or prompt injection testing experience- Familiarity with RAG evaluation frameworks and groundedness scoring- Statistical background: clustering effects, effective sample size, stratified sampling- Exposure to SageMaker Model Monitor, MLflow or comparable drift and experiment tracking- Security testing certification or demonstrable practice (ref:hirist.tech)

📌 AI QA Engineer (Bengaluru)
🏢 V2Soft
📍 Bengaluru