QA Engineer – Automation & AI Expert

Fiserv•Alpharetta, GA

About The Position

Lead quality engineering and release coordination for a high-volume portfolio of mainframe and integrated applications. The role emphasizes scalable test automation, responsible use of AI, risk-based testing, and cross-functional delivery across the SDLC and STLC.

Requirements

6+ years of experience designing or coordinating automated test solutions, reusable frameworks, regression suites, test-data management, and results reporting.
3+ years of experience in generative AI and AI-assisted quality engineering, including prompting, context preparation, output evaluation, and responsible-use practices.
Strong knowledge of SDLC and STLC practices, requirements analysis, test planning, execution, defect management, and release readiness.
Ability to evaluate automation candidates using business value, risk, repeatability, complexity, maintenance cost, and expected return.
Strong leadership, organization, risk analysis, stakeholder management, and written and verbal communication skills.
Bachelor’s degree in Computer Science, Information Technology, Information Systems, or a related field, or equivalent experience and certifications.
Must currently possess valid and unrestricted U.S. work authorization.

Nice To Haves

Hands-on IBM mainframe testing experience, with emphasis on JCL and familiarity with technologies such as COBOL, DB2, VSAM, CICS, IMS, TSO/ISPF, REXX, CA7, Endevor, FILE-AID, and debugging tools.
Experience in payment processing, acquirer systems, association or network certification, and change-release testing.
Experience with JIRA, Kanban, delivery pipelines, and scheduled mainframe automation.

Responsibilities

Design, implement, and continuously improve automated testing for mainframe and integrated applications, including reusable regression suites, test-data validation, batch workflows, and release-readiness checks.
Apply approved AI tools to accelerate requirements analysis, test-case generation, coverage-gap detection, defect triage, failure analysis, documentation, and status reporting.
Review AI-generated outputs for accuracy, completeness, traceability, security, and business relevance while retaining human accountability for quality decisions.
Identify high-value automation opportunities, define success measures, and partner with technical teams to pilot and scale solutions that improve coverage, speed, and reliability.
Coordinate concurrent projects and recurring releases, including test planning, environment and data readiness, defect resolution, risk management, stakeholder communication, and signoff.
Maintain traceability and provide clear reporting on test progress, defects, risks, quality metrics, and release health.