AVP, Software Development Engineer in Test (SDET)

What if you could build a career where ambition meets innovation? At LPL Financial, we empower professionals to shape their success while helping clients pursue their financial goals with confidence. What if you could have access to cutting-edge resources, a collaborative environment, and the freedom to make an impact? If you're ready to take the next step, discover what’s possible with LPL Financial.

Job Overview:

As an AVP, Software Development Engineer in Test (SDET) you will join a team of talented developers in test and work on next-generation application software to enhance our engineering best practices, tools, design patterns and frameworks.

This leader will partner with software engineers to understand product architecture and integrate AI-based quality gates and observability into CI/CD pipelines. While creating and maintaining synthetic and production-like data scenarios using AI-powered data generation tools to support comprehensive test coverage.

Responsibilities:

• Collaborate with product, engineering, and cross-functional domain teams to understand features, develop and automate test cases, and drive continuous improvement in product quality and release velocity.

• Apply diverse software testing methodologies—including AI-driven test generation and predictive analytics—to ensure robust and scalable software delivery.

• Coordinate and lead end-to-end (E2E) testing efforts across domains, leveraging AI tools for test orchestration, anomaly detection, and intelligent defect triaging.

• Identify manual processes and implement intelligent automation solutions using AI/ML frameworks to optimize testing efficiency and reduce cycle time.

• Participate in code reviews, advocating for clean code principles and AI-assisted static analysis tools to enhance code quality and maintainability.

• Analyze test results using AI/ML models to detect patterns, predict defect-prone areas, and proactively improve test strategies.

• Document and track bugs to closure, incorporating AI-based prioritization and root cause analysis to streamline defect management.

• Operate within Agile/Scrum frameworks, integrating AI tools for sprint planning, test impact analysis, and continuous feedback loops.

• Mentor and coach teams on best practices in test automation, AI/ML integration in QE, and scalable test architecture design.

What are we looking for?

We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

Requirements:

• Master’s or Bachelor’s Degree in Computer Science or a related field, with a strong foundation in software engineering principles and AI/ML fundamentals.

• Minimum of 10 years of hands-on experience in software development and test automation, with advanced concurrent programming skills in Java/Python and exposure to AI-assisted development tools.

• Minimum of 6 years of leadership experience managing multiple initiatives, including AI-driven quality engineering programs and cross-functional automation strategies.

• Minimum of 4 years of experience designing and building scalable automation frameworks, with a focus on integrating AI/ML for intelligent test generation and maintenance.

• Minimum of 4 years of experience in API testing (SOAP and REST/Microservices), including the use of AI tools for contract validation and anomaly detection.

• Minimum of 4 years of experience with test frameworks such as JUnit, TestNG, and tools for UI/Mobile testing, enhanced by AI-based visual validation and test optimization.

• Proven experience with CI/CD pipelines, including integration of AI for test impact analysis, release risk prediction, and automated quality gates.

• Proficient in Agile tools like Jira, with experience using AI plugins for sprint planning, backlog grooming, and defect prediction.

• Skilled in defect and test management tools, with a preference for platforms that support AI-based prioritization and root cause analysis.

• Strong SQL expertise, capable of writing complex queries and leveraging AI for data validation, anomaly detection, and test data generation.

• Familiarity with BDD frameworks like Cucumber, with experience in integrating NLP-based AI tools for scenario generation and test coverage analysis.

• Experience develop