Python QA with AI/ML Experience

Infobeans; Indore District, Pune District, Bengaluru; On-site; INR 300,000 - 540,000; Full time

Job summary

Infobeans 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.

The role combines data engineering QA, ML lifecycle validation, and platform testing in cloud-native environments.

Qualifications

• 7+ years of QA or data validation experience in AI/ML or data platforms.
• Hands-on Databricks experience with Spark, Delta Lake, MLflow.
• Strong Python for testing and automation.
• Understanding of the ML lifecycle including features, training, validation, deployment.
• Experience testing data pipelines, large datasets, and batch/realtime execution.

Responsibilities

• Validate data 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 regulatory standards.
• Conduct regression, performance, and volume testing for production workloads.
• Build automated regression suites and integrate QA into CI/CD for ML workflows.

Skills

QA/Data validation

Python scripting

Collaboration with data scientists

Education

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

Tools

Databricks (Spark, Delta)

MLflow

PySpark

CI/CD tooling

Job description

Job Description: AI/ML QA Specialist with strong Databricks experience

Job summary:

We are seeking for 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 endtoend testing of CCAR and ESG projects, covering data pipelines, feature engineering, model training, validation, deployment, and monitoring. The ideal candidate blends data engineering QA, ML lifecycle validation, and platform testing in cloudnative environments. The plan is to automate the QA Testing and make it part of the regression suite that will be run for every future deployment.

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, and lineage
    • Feature consistency and drift checks
    • Reproducibility of model training and experiments
  • 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 after updates or enhancements.
• Model Execution / Production Platform QA
  • Test model deployment pipelines, including batch and realtime model execution.
  • Validate:
    • Model scoring accuracy and performance
    • Input/output data contracts and SLAs
    • Error handling, fallback logic, and retries
  • Perform regression, performance, and volume testing for production workloads.
  • Validate monitoring metrics (model health, drift, latency, failures).
  • Conduct regression testing to ensure existing functionality remains unaffected after updates or enhancements.
• Automation & Tooling
  • Build and maintain automated test frameworks for data and ML pipelines (Databricks notebooks, PySpark, Python).
  • Implement datadriven 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 nontechnical stakeholders.

Required Skills & Qualifications

• 58+ years of QA or data validation experience, with strong focus on AI/ML or data platforms
• Handson experience with Databricks:
  • Spark / PySpark
  • Delta Lake
  • MLflow
• Strong Python experience for testing and automation
• Solid understanding of the ML lifecycle (data features training validation deployment)
• Experience testing:
  • Data pipelines and largescale datasets
  • Batch and realtime model execution
• Knowledge of cloud platforms (Azure preferred)
• Familiarity with CI/CD, Git, and automated testing frameworks

Preferred / NicetoHave

• Experience with model risk management (MRM) or regulated environments (banking, risk, compliance).
• Exposure to:
  • Feature stores
  • Model monitoring and drift detection
  • Power BI or downstream analytical reporting validation
• Experience with performance testing at scale in distributed environments.
• Prior work on platform modernization or cloud migration initiatives.

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.