AI & Data Validation Engineer — Ground-Truth QA Automation

RADcube - A NLogix Company; Indianapolis (IN); On-site; USD 90,000 - 120,000; Full time

Job summary

RADcube - A NLogix Company is seeking a Validation Engineer, AI & Data in Indianapolis, IN. You will validate the accuracy and reliability of AI-generated responses against ground truth and approved requirements.

Responsibilities include designing testing strategies for domain context models, building automated evaluation tests, and ensuring data quality, SQL validation, and regression testing across AI systems.

Qualifications

• Experience with data quality engineering and SQL-based validation.
• Proficient Python scripting for automated tests.
• Familiarity with AI/LLM evaluation and ground-truth testing.

Responsibilities

• Design and implement testing strategies for the Domain Context Model and related AI components.
• Create ground truth QA test sets with defined tolerances and ownership validation.
• Build automated evaluation tests for AI responses via the Context Injection toolkit.
• Evaluate AI outputs with/without domain context to establish baselines and improvements.
• Validate numeric results, SQL queries, business logic, and narrative responses.
• Identify and classify failures including hallucinations and schema issues.
• Develop repeatable evaluation scripts, scoring rubrics, and dashboards.
• Verify context repository data completeness and access control behavior.
• Document test evidence, defects, remediation results, and readiness assessments.
• Support release readiness and demonstrations with reusable evaluation docs.

Skills

Data quality

SQL

Python

Automated testing

PyTest

ETL

Data validation

AI evaluation

Ground truth testing

Regression testing

Test automation

Job description

RADcube - A NLogix Company is seeking a Validation Engineer, AI & Data in Indianapolis, IN. You will validate the accuracy and reliability of AI-generated responses against ground truth and approved requirements.

Responsibilities include designing testing strategies for domain context models, building automated evaluation tests, and ensuring data quality, SQL validation, and regression testing across AI systems.