Job Description

Job Title-AI Quality Engineer
Location - Saint Louis, MO
Number of days onsite-3 Days
Rate :: 45-50/hr on w2 , $57-60/hr on C@C Max

8210942368_1 and VMS #: 21179-1

Must Have Skills
Drift Detection

Java

Selenium

Cucumber

Cloud

Google Apps

UiPath

AI/ML,

Google kubernates Engineering

Recommended Qualification and Experience:

• Experience - 5+ years in Software Quality Engineering, with at least 2 years focused on AI/ML, UiPath automation testing.
• Understanding of OCR (Optical Character Recognition), NLP (Natural Language Processing), and LLM architectures if Value Add Experience
• Possesses prior experience in Java OR Python scripting , as well as experience with Salesforce and GCP (Google Cloud Platform)

Proficient in system integrations utilizing technologies such as SQL, REST APIs, and Google Kubernetes Engine

Role : AI Quality Engineer

• We are looking for a highly experienced and certified AI Quality Engineer who will drive transformation within our team.
• The ideal candidate will oversee the validation and lifecycle of our generative and predictive models.
• Your mission is to ensure our AI systems are not just smart but are enough for production.
• What You'll DoThe responsibilities of an AI Quality Engineer are split between testing the AI itself, AI-Driven Automation and using effective Prompt engineering to improve testing and Quality processes.
• This role also involves engaging and collaborating with cross-functional teams
• Your key responsibilities will include
• Define and execute end-to-end testing and validation frameworks for AI services, encompassing machine learning models, agentic workflows, and automation solutions.
• Focus on rigorous model and data validation, real-time observability, and the implementation of autonomous test generation
• Data Quality Auditing: Validating training and testing datasets for bias, noise, and completeness
• Model Performance Testing: Defining and tracking metrics beyond simple such as Precision-Recall curves, F1-score, and Mean Absolute Error (MAE).
• Adversarial Testing: Intentionally providing &apos prompt injections or perturbed images to see if the model breaks or leaks sensitive data
• Drift Detection: Monitoring production models for concept drift (changes in real-world data patterns) and data
• Fairness Monitoring: Ensuring the model's outputs remain ethical and do not discriminate against specific demographics over time
• You will be the primary gatekeeper for AI Governance and compliance, ensuring the model adheres to Equifax AI standards.
• Design comprehensive test cases and strategic test plans to validate RPA solutions (UiPath) for both new implementations and existing systems.
• Document detected defects and manage their lifecycle through to resolution if !supportListsValidate the Hand-off points where an AI Agent triggers an PRA bot to perform a structural task.

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