Full Description

When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

Summary

• In this role, you will validate AI and ML-powered healthcare solutions across the full development lifecycle to ensure data quality, model performance, reliability, and safe deployment in production environments. You will design and execute data-driven and automated test strategies, including model evaluation, prompt regression testing, dataset profiling, and end-to-end pipeline validation. You will partner with data science, engineering, product, and security teams to define measurable quality gates and deliver compliant, explainable, and dependable AI experiences that drive client value.

Responsibilities

• Develop and execute test strategies for ML and generative AI-powered applications.
• Design and maintain evaluation frameworks for Large Language Models (LLM), including automated scoring and LLM -as-a-judge methodologies.
• Develop prompt regression test suites to detect performance degradation across model and prompt versions.
• Evaluate generative AI systems for hallucination risk, factual consistency, grounding accuracy, and safety compliance.
• Conduct model evaluation, regression testing, and drift monitoring in development and production environments.
• Build dashboards and monitoring tools to detect degraded evaluation scores, drift, or safety risks and support proactive triage.
• Design and implement proactive AI-driven alerting and recommendation systems embedded within dashboards and user workflows
• Automate dashboard metric generation and refresh pipelines using Python and data workflows.
• Partner with cross-functional teams to define AI quality standards, acceptance criteria, and release gates.
• Investigate defects, analyze root causes, and recommend corrective actions to improve reliability and performance.

Qualifications

• Relevant degree preferred.
• 2 or more years of relevant experience required.
• Experience validating ML or generative AI-based applications, including model evaluation and data quality assessment required.
• Proficiency in Python, SQL, and test automation frameworks.
• Experience evaluating LLM systems, including prompt regression testing and automated or human-in-the-loop judging methodologies.