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 is seeking skilled AI/ML QA specialists with Databricks expertise to ensure quality and regulatory readiness for CCAR/ESG projects. You will cover data pipelines, feature engineering, model training, validation, deployment, and monitoring.

Responsibilities include validating ingestion, feature engineering, MLflow experiments, and model versioning, plus testing deployment pipelines and monitoring SLAs. Strong Python and Azure experience preferred.

Qualifications

• 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.

Responsibilities

• 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.
• 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.
• 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.

Skills

Databricks

PySpark

MLflow

Python

Model lifecycle

Azure

Education

Bachelor's or Master's degree in CS/DS/Engineering

Tools

Spark

Delta Lake

Job description

Job Description:

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)

Requirements: