Validation Engineer - AI & Data

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

Job summary

RADcube in Indianapolis seeks a Data Quality Engineer to validate the accuracy and reliability of business context and AI responses against approved requirements. You will design testing strategies and develop robust QA processes while collaborating with data owners to ensure ground truth integrity.

Responsibilities include building automated evaluation tests with PyTest, developing ground truth sets, and delivering dashboards for accuracy and release readiness.

Qualifications

• Proficient in SQL and Python for data QA and testing.
• Experience with automated testing frameworks and PyTest.
• Knowledge of ETL processes and data validation techniques.
• Familiarity with AI/LLM evaluation and ground-truth testing.
• Experience with regression testing and test automation.

Responsibilities

• Design the testing strategy for the Domain Context Model, Business Context Repository, ontology mappings, and AI response evaluation.
• Develop ground truth question/answer test sets with defined results and tolerances, validated by data owners.
• Build and maintain automated evaluation tests for AI responses via the Context Injection toolkit.
• Evaluate AI responses with/without domain context to establish baselines and improvements.
• Validate numeric answers, SQL results, business logic, and data relationships.

Skills

SQL

Python

Automated testing

ETL

Data validation

AI evaluation

Ground truth testing

Regression testing

Test automation

Tools

PyTest

ETL tools

Job description

Summary: Validate the accuracy, reliability, completeness, and consistency of business context and AI generated responses against approved business requirements and ground truth answers.

Responsibilities

• Design and implement the testing strategy for the Domain Context Model, Business Context Repository, ontology mappings, and AI response evaluation.
• Develop representative ground truth question and answer test sets with defined expected results and tolerances, validated by data owners.
• Build and maintain automated evaluation tests for AI responses generated through the client's Context Injection toolkit.
• Evaluate AI responses with and without domain context to establish baseline accuracy and measure improvements.
• Validate numeric answers, SQL and query results, business logic, aggregations, join relationships, and narrative responses.
• Identify and classify failures, including hallucinations, stale information, incorrect schemas, faulty joins, missing business context, and persona mismatches.
• Develop repeatable evaluation scripts, scoring rubrics, regression tests, and accuracy reporting dashboards or scorecards.
• Verify context repository data completeness, classification consistency, access control behavior, and changes introduced through automated context updates.
• Document test evidence, defects, remediation results, AI readiness assessments, and validation outcomes.
• Support release readiness, weekly demonstrations, and handover of reusable evaluation frameworks and documentation.

Required Technical Expertise: Data quality engineering, SQL, Python, automated testing, PyTest or equivalent frameworks, ETL and data validation, AI and LLM evaluation, ground truth testing, regression testing, and test automation.