Senior Quality Analyst, Assistant Manager

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

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits

• 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.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
• 5+ years of experience in QA, Quality Engineering, or Test Automation roles.
• Hands-on experience implementing AI/ML-enabled solutions within QA or SDLC processes.
• Strong understanding of software testing methodologies, SDLC, STLC, Agile, and DevOps practices.
• Experience with test automation tools/frameworks such as Selenium, Playwright, Cypress, UFT, or similar.
• Strong experience with API testing, database validation, and backend testing.
• Experience using AI-assisted development/testing tools such as GitHub Copilot, OpenAI APIs, Azure AI, LangChain, Testim, Mabl, Functionize, or similar platforms.
• Proficiency in at least one programming/scripting language such as Python, Java, JavaScript, or C#.
• Experience integrating automated testing into CI/CD pipelines using tools like Azure DevOps, Jenkins, GitHub Actions, or similar.
• Strong analytical, problem-solving, and communication skills.

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.

*The posted base salary range is the hiring base salary range for this role.

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.