InfoBeans - AI/ML Quality Assurance Specialist - Python

InfoBeans Inc.; Indore District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

InfoBeans Inc. in Indore seeks an AI/ML QA Specialist with strong Databricks experience to ensure quality, reliability, and regulatory readiness of AI/ML platforms.

You will focus on end-to-end testing of CCAR and ESG projects, covering data pipelines, feature engineering, model training, validation, deployment, and monitoring. Responsibilities include validating data ingestion, feature engineering and training pipelines on Databricks, designing QA strategies for data quality and drift checks,

Qualifications

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

Responsibilities

• Validate ingestion, feature engineering, and training pipelines on Databricks.
• Design QA strategies for data quality, schema validation, lineage and drift checks.
• Test MLflow experiments, model versioning, and artifacts for traceability.
• Ensure compliance with model risk management, audit and documentation standards.
• Test model deployment pipelines and validate scoring accuracy and SLAs.
• Perform regression, performance and volume testing for production workloads.
• Build automated test frameworks for data and ML pipelines (Databricks notebooks, PySpark, Python).
• Implement data-driven QA checks (DQ rules, nulls, thresholds).
• Integrate QA into CI/CD pipelines for ML workflows.

Skills

QA / data validation

Python testing

ML lifecycle

Azure (cloud)

Education

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field

Tools

Databricks (Spark/PySpark)

Delta Lake

MLflow

PySpark

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)