AI Quality Engineer

Job Title-AI Quality Engineer Saint Louis, MO 3 DAYS Mandatory Areas/ Must Have Skills: Drift Detection UIPath Google kubernates Engineering Role: AI Quality Engineer We are seeking an experienced AI Quality Engineer to lead quality assurance and validation across our AI-driven platforms. This role is critical to ensuring our generative and predictive AI systems are production-ready, ethically responsible, and high-performing at scale. You will own the quality lifecycle of AI models and intelligent automation solutions, ensuring reliability, compliance, and trustworthiness across environments. What You'll Do As an AI Quality Engineer, your responsibilities span AI model validation, AI-driven automation testing, and the use of advanced prompt engineering to enhance testing effectiveness and quality processes. You will collaborate closely with engineering, data science, automation, and business teams to embed quality throughout the AI lifecycle. Key Responsibilities: AI Testing & Validation Frameworks: Define and execute comprehensive, end-to-end testing and validation frameworks for AI services, including machine learning models, agentic workflows, and automation solutions. Emphasize robust model and data validation, real-time observability, and autonomous test generation. Data Quality Auditing: Assess training and test datasets for bias, noise, completeness, and representativeness, ensuring data integrity and suitability for production models. Model Performance Evaluation: Design and track advanced evaluation metrics beyond binary pass/fail, including Precision-Recall, F1-score, MAE, and other model-specific performance indicators. Adversarial & Security Testing: Perform adversarial testing such as prompt injection attacks and input perturbations to evaluate model robustness, security, and potential data leakage risks. Drift Detection & Monitoring: Monitor production models for data drift and concept drift, proactively identifying performance degradation caused by changing real-world data patterns. Bias, Fairness & Ethics Monitoring: Continuously validate that model outputs remain fair, ethical, and non-discriminatory, aligned with organizational and regulatory standards. AI Governance & Compliance: Act as the primary quality gatekeeper for AI governance, ensuring compliance with Equifax AI standards, policies, and audit requirements. Automation Quality Assurance (RPA): Design and execute test plans for UiPath RPA solutions, covering both new implementations and existing automations. Log, track, and manage defects through resolution. AI-Automation Integration Validation: Validate critical handoff points where AI agents trigger RPA bots to execute structured tasks, ensuring seamless orchestration and reliability. Qualifications & Experience Experience: Software Quality Engineering, with focused on AI/ML systems and UiPath automation testing. AI & Data Foundations: Strong understanding of OCR, NLP, and LLM architectures, including their testing, limitations, and failure modes. Value-Added Skills: Experience with Java and Python scripting, along with exposure to Salesforce and Google Cloud Platform (GCP). Proficiency in system integrations using SQL, REST APIs, and Google Kubernetes Engine (GKE).

Created: 2026-03-04