QA Automation Engineer – Agentic AI, UI, and API

Vizient•Chicago, IL

2d•$102,400 - $179,000

About The Position

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.

Requirements

AI-powered applications
agentic AI systems
user interfaces
APIs
LLM-enabled workflows
intelligent automation
integrated enterprise platforms
scalable test automation frameworks
reusable testing accelerators
validation processes
automated workflows
AI validation approaches
functional testing
prompt testing
workflow testing
integration testing
regression testing
human-in-the-loop testing
validation datasets
quality evaluation methods
AI-assisted testing
modern quality engineering practices
nondeterministic AI behavior
intelligent orchestration
evolving healthcare workflows
runtime quality and reliability
telemetry
distributed tracing
observability
monitoring
drift detection
incident analysis
production validation
engineering
AIOps
LLMOps
security
governance
data
clinical
operational clients
software and AI development lifecycle
test execution
defect management
release validation
risk identification
production support activities
testing tools
automation tools
observability tools
AI quality engineering tools
scalability
reliability
traceability
technical guidance
automation practices
continuous learning
engineering discipline
quality risks
technical findings
implementation dependencies
recommendations
technical clients
business clients
continuous improvement
responsible AI adoption

Responsibilities

Design and execute quality engineering strategies for AI-powered applications, agentic AI systems, user interfaces, APIs, LLM-enabled workflows, intelligent automation, and integrated enterprise platforms.
Develop scalable test automation frameworks, reusable testing accelerators, validation processes, and automated workflows that improve quality, efficiency, and test coverage.
Implement AI validation approaches, including functional, prompt, workflow, integration, regression, and human-in-the-loop testing, along with validation datasets and quality evaluation methods.
Advance AI-assisted testing and modern quality engineering practices that address nondeterministic AI behavior, intelligent orchestration, and evolving healthcare workflows.
Support runtime quality and reliability through telemetry, distributed tracing, observability, monitoring, drift detection, incident analysis, and production validation.
Collaborate with engineering, AIOps, LLMOps, security, governance, data, clinical, and operational clients to integrate quality practices throughout the software and AI development lifecycle.
Coordinate test execution, defect management, release validation, risk identification, and production support activities across AI-enabled solutions.
Evaluate and implement testing, automation, observability, and AI quality engineering tools and technologies that improve scalability, reliability, and traceability.
Mentor quality engineers and analysts by providing technical guidance, sharing automation practices, and fostering continuous learning and engineering discipline.
Communicate quality risks, technical findings, implementation dependencies, and recommendations to technical and business clients while promoting continuous improvement and responsible AI adoption.