InfoBeans Technologies - AI/ML Quality Assurance Specialist

• Validate data ingestion, feature engineering, and training pipelines built on Databricks (Spark, Delta, MLflow).
• Design and execute QA strategies for dataset quality, schema validation, lineage, feature consistency, drift checks, and reproducibility of model training.
• Test MLflow experiments, model versioning, and artifacts for completeness and traceability.
• Ensure compliance with model risk management (MRM), audit, and documentation standards.
• Conduct regression testing to ensure existing functionality remains unaffected.

Model Execution / Production Platform QA

• Test model deployment pipelines, including batch and real-time model execution.
• Validate model scoring accuracy, performance, input/output data contracts, SLAs, error handling, fallback logic, and retries.
• Perform regression, performance, and volume testing for production workloads.
• Validate monitoring metrics (model health, drift, latency, failures).

Automation & Tooling

• Build and maintain automated test frameworks for data and ML pipelines (Databricks notebooks, PySpark, Python).
• Implement data-driven QA checks (DQ rules, nulls, thresholds, statistical validation).
• Integrate QA into CI/CD pipelines for ML workflows.
• Build automated regression suite to be run prior to any deployments.

Governance & Collaboration

• Partner with Data Scientists, ML Engineers, Platform Engineers, and Model Risk teams.
• Support UAT, audit reviews, and regulatory validation initiatives.
• Document QA results clearly for technical and non-technical stakeholders.

Required Skills & Qualifications

• 5 - 8+ years of QA or data validation experience, with strong focus on AI/ML or data platforms.
• Hands-on experience with Databricks (Spark/PySpark, Delta Lake, MLflow).
• Strong Python experience for testing and automation.
• Solid understanding of the ML lifecycle.
• Experience testing data pipelines, large-scale datasets, and model execution.
• Knowledge of cloud platforms (Azure preferred).
• Familiarity with CI/CD, Git, and automated testing frameworks.

Preferred / Nice-to-Have

• Experience with model risk management (MRM) or regulated environments.
• Exposure to feature stores, model monitoring, drift detection, and Power BI.
• Experience with performance testing at scale in distributed environments.
• Prior work on platform modernization or cloud migration initiatives.

Education

• Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field.

(ref:hirist.tech)