Senior AI QA Engineer
RhythmX AI (company site, opens in a new tab)6+ yrsBengaluruOn-siteDirect career pageEst. 18–28 LPA
Posted 20h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at RhythmX AI
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at RhythmX AI you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Design and implement multi-layered validation frameworks combining deterministic, semantic similarity, and model-based evaluations for clinical AI outputs.
- Develop automated grading systems to assess clinical pipeline output faithfulness, safety, and hallucination detection using NLP metrics and custom heuristics.
- Execute integration and regression tests to ensure backend medical data pipeline integrity and perform root cause analysis of system failures.
Minimum Requirements
- 6+ years of technical experience in Quality Assurance with focus on system architecture and backend data validation.
- Expert-level Python programming skills for custom test scripting in AI/ML ecosystems.
- Proven experience validating AI/ML model outputs using semantic similarity, NLP metrics, and custom scoring algorithms; familiarity with LangSmith, Arize Phoenix, or MLflow.
- Proficiency with AI-native development tools (Cursor, Windsurf, Claude Code), modern automation frameworks (PyTest), API testing tools (Postman/RestAssured), and database validation (SQL/NoSQL).
Ideal Candidate Profile
- Experienced in building and owning complex test architectures for AI-driven clinical systems requiring high accuracy and safety.
- Skilled at using advanced AI-native and automation tools to prototype and implement scalable validation solutions rapidly.
- Collaborative with experience working alongside Data Scientists and Product Managers to define evaluation metrics and clinical 'Ground Truth' datasets.