We are seeking an experienced QA professional with strong expertise in Quality Engineering and practical experience implementing AI-driven solutions across the Software Development Lifecycle (SDLC). This role will be responsible for embedding AI into QA processes, improving product quality proactively, and driving intelligent automation initiatives that reduce manual effort, improve defect prevention, and accelerate delivery timelines. The ideal candidate will possess a strong understanding of product functionality, business workflows, testing methodologies, automation frameworks, and emerging AI technologies applicable to Quality Assurance and engineering operations.
Base salary range $73,000 - $110,000
The posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate''s skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world's leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL's Human Resources team, as well as our hiring managers.
Required Qualifications:
Preferred Qualifications
U.S Healthcare experience in Payment Integrity, Claims processing, Adjudication, etc.
Experience developing predictive analytics or anomaly detection solutions for quality monitoring.
Exposure to Generative AI, LLM-based workflows, prompt engineering, or AI agents in engineering operations.
Experience in healthcare, payment integrity, audit, or enterprise platform environments.
Knowledge of cloud platforms such as Azure, AWS, or GCP.
Experience with Power BI, reporting, and operational insights generation is a plus.
Partner with Product, Engineering, Operations, and Business teams to gain deep understanding of product workflows, business rules, integrations, and risk areas.
Design and implement AI-enabled QA solutions that help identify, predict, and prevent defects early in the SDLC.
Embed AI capabilities into the product and QA ecosystem to improve monitoring, validation, anomaly detection, regression analysis, and quality insights.
Leverage AI tools and frameworks for:
Intelligent test case generation
Automated test script creation and maintenance
Defect prediction and root cause analysis
Test optimization and prioritization
Requirement-to-test traceability
Regression impact analysis
Automated validation and data quality checks
Build and enhance automated testing frameworks for UI, API, database, and batch processing systems.
Drive shift-left quality practices and integrate AI-enabled testing into CI/CD pipelines.
Analyze production trends, defect leakage, audit findings, and operational data to recommend preventive quality controls.
Collaborate with development teams to implement proactive quality gates and self-healing automation solutions.
Evaluate emerging AI technologies, tools, and accelerators for adoption within QA and engineering processes.
Create dashboards, reporting, and metrics demonstrating QA efficiency gains, defect reduction, automation coverage, and AI-driven improvements.
Mentor QA team members on AI-assisted testing approaches and modern quality engineering practices.
Partner with Product, Engineering, Operations, and Business teams to gain deep understanding of product workflows, business rules, integrations, and risk areas.
Design and implement AI-enabled QA solutions that help identify, predict, and prevent defects early in the SDLC.
Embed AI capabilities into the product and QA ecosystem to improve monitoring, validation, anomaly detection, regression analysis, and quality insights.
Leverage AI tools and frameworks for:
Intelligent test case generation
Automated test script creation and maintenance
Defect prediction and root cause analysis
Test optimization and prioritization
Requirement-to-test traceability
Regression impact analysis
Automated validation and data quality checks
Build and enhance automated testing frameworks for UI, API, database, and batch processing systems.
Drive shift-left quality practices and integrate AI-enabled testing into CI/CD pipelines.
Analyze production trends, defect leakage, audit findings, and operational data to recommend preventive quality controls.
Collaborate with development teams to implement proactive quality gates and self-healing automation solutions.
Evaluate emerging AI technologies, tools, and accelerators for adoption within QA and engineering processes.
Create dashboards, reporting, and metrics demonstrating QA efficiency gains, defect reduction, automation coverage, and AI-driven improvements.
Mentor QA team members on AI-assisted testing approaches and modern quality engineering practices.