InfoBeans - AI/ML Quality Assurance Specialist - Python

Job Description

We are seeking skilled AI/ML QA Specialists with strong Databricks experience to ensure the quality, reliability, and regulatory readiness of AI/ML platforms. This role will focus on end-to-end testing of CCAR and ESG projects, covering data pipelines, feature engineering, model training, validation, deployment, and monitoring.

Key Responsibilities

Model Development Platform QA :

• 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.
• Test MLflow experiments, model versioning, and artifacts for completeness and traceability.
• Ensure compliance with model risk management (MRM), audit, and documentation standards.
  
  

Model Execution / Production Platform QA

• Test model deployment pipelines, including batch and real-time model execution.
• Validate model scoring accuracy, performance, data contracts, SLAs, error handling, and fallback logic.
• Perform regression, performance, and volume testing for production workloads.
  
  

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.
  
  

Required Skills

• 5 - 8+ years of QA or data validation experience.
• Hands-on experience with Databricks (Spark/PySpark, Delta Lake, MLflow).
• Strong Python experience for testing and automation.
• Solid understanding of the ML lifecycle.
• Knowledge of cloud platforms (Azure preferred).
  
  

Preferred Skills

• Experience with model risk management (MRM) or regulated environments.
• Exposure to feature stores, model monitoring, and drift detection.
• Experience with performance testing at scale in distributed environments.
  
  

Education

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

(ref:hirist.tech)