AI/ML QA Specialist

Role Overview

Infobeans is looking for an experienced AI/ML QA Specialist with deep Databricks exposure to help verify the quality, stability, and compliance readiness of AI/ML platforms. The role centers on end-to-end testing for CCAR and ESG initiatives, covering everything from data pipelines and feature creation to model training, validation, release, and ongoing monitoring. This position is aimed at someone who can bridge data engineering quality checks, ML lifecycle validation, and platform testing in cloud-based environments. A key objective is to automate QA checks and fold them into the regression suite that will run before future deployments.

Key Responsibilities

• Review and validate data ingestion flows, feature engineering steps, and model training pipelines built on Databricks using Spark, Delta Lake, and MLflow.
• Create and run QA approaches for dataset integrity, schema checks, and lineage verification.
• Check feature stability, consistency, and drift-related behavior.
• Verify that training runs and experiments are reproducible across executions.
• Test MLflow experiments, model versions, and stored artifacts to confirm completeness and traceability.
• Support model risk management, audit, and documentation expectations.
• Carry out regression testing so enhancements or fixes do not break existing functionality.
• Validate model deployment pipelines for both batch and real-time execution.
• Assess model scoring accuracy, performance, and data contract/SLA compliance.
• Test error recovery paths, fallback behavior, and retry mechanisms.
• Perform regression, performance, and volume testing for production-grade workloads.
• Review monitoring signals such as model health, drift, latency, and failure rates.
• Develop and maintain automated test frameworks for data and ML pipelines using Databricks notebooks, PySpark, and Python.
• Implement data-led QA controls including DQ rules, null checks, thresholds, and statistical checks.
• Embed QA into CI/CD flows for ML releases.
• Build an automated regression suite to run before each deployment.
• Work closely with Data Scientists, ML Engineers, Platform Engineers, and Model Risk teams.
• Assist with UAT, audit reviews, and regulatory validation activities.
• Prepare QA findings and results in a clear format for both technical and non-technical audiences.

Requirements

• 5–8+ years of experience in QA or data validation, with a strong focus on AI/ML or data platforms.
• Practical hands-on expertise with Databricks, including Spark/PySpark, Delta Lake, and MLflow.
• Strong Python skills for automation and testing.
• Good understanding of the ML lifecycle, including data, feature engineering, training, validation, and deployment.
• Experience testing data pipelines and large-scale datasets.
• Experience validating both batch and real-time model execution.
• Working knowledge of cloud environments, with Azure preferred.
• Familiarity with CI/CD, Git, and automated testing toolsets.
• Experience in model risk management or other regulated settings such as banking, risk, or compliance is a plus.
• Exposure to feature stores, model monitoring, drift detection, or reporting validation tools like Power BI is preferred.
• Ability to perform performance testing in distributed environments.
• Experience with platform modernization or cloud migration programs is advantageous.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related discipline.

Additional Information

This role is based in Indore, India. Graduation is not mandatory for applicants.

Work Focus

The QA effort is expected to become part of the long-term regression framework so it can be reused for future releases and deployments.

Company

InfoBeans Technologies Limited.