Applied ML QA Engineer

Compunnel, Inc.; Louisville (KY); On-site; USD 100,000 - 130,000; Part time

Job summary

A technology company in the United States is seeking an experienced Applied ML QA Engineer. The role focuses on validating machine learning models, developing automated test frameworks, and ensuring robust model deployment. Candidates should have substantial experience in Python, ML QA, and drift detection, along with familiarity with tools like Databricks. Strong analytical and problem-solving skills are also essential. This is a contractor position offering opportunities to work with innovative technologies.

Qualifications

• 8+ years of experience in Python, ML QA, drift detection, and testing ML features/pipelines.
• 5+ years of experience with Databricks, feature stores, and model serving.
• Strong understanding of ML lifecycle, including testing and monitoring.

Responsibilities

• Validate ML models, scoring outputs, and feature pipelines for accuracy and reliability.
• Develop and maintain automated test frameworks for ML workflows.
• Collaborate with data scientists and engineers to ensure robust model deployment.

Skills

Python

ML QA

Drift detection

Testing ML features/pipelines

Analytical skills

Problem-solving skills

Tools

Databricks

GenAI tools

ML observability tools

Job description

We are seeking an experienced Applied ML QA Engineer (Contractor) to validate machine learning models, scoring outputs, feature pipelines, and inference services for a real-time decisioning platform.

This role involves creating automated tests, implementing drift monitoring, performing consistency checks, and providing validation support for every model release.

Key Responsibilities

• Validate ML models, scoring outputs, and feature pipelines for accuracy and reliability.
• Develop and maintain automated test frameworks for ML workflows.
• Perform consistency checks across training, validation, and inference environments.
• Collaborate with data scientists and engineers to ensure robust model deployment.
• Maintain documentation and validation artifacts for compliance and reproducibility.

Required Qualifications

• 8+ years of experience in Python, ML QA, drift detection, and testing ML features/pipelines.
• 5+ years of experience with Databricks, feature stores, and model serving (preferred).
• 5+ years of experience using GenAI tools (Cursor, Windsurf, Copilot).
• Strong understanding of ML lifecycle, including testing and monitoring.
• Familiarity with CI/CD workflows for ML deployments.

Preferred Qualifications

• Experience with real-time decisioning platforms.
• Knowledge of ML observability tools and QA best practices.
• Strong analytical and problem-solving skills.