Lead AI Quality Assurance Engineer

The Quality Assurance Engineering team plays a critical role in ensuring the reliability, security, and effectiveness of Envestnet’s technology solutions, with a growing focus on AI-enabled products and platforms. Working at the intersection of engineering, data science, product, cybersecurity, and business operations, the team develops and executes innovative testing and validation strategies that help deliver trusted experiences for clients and internal stakeholders alike. By championing quality, governance, automation, and continuous improvement, the team helps accelerate the adoption of emerging technologies while ensuring solutions are scalable, compliant, and built to meet the highest standards of performance and customer confidence.

Ensures that artificial intelligence solutions are accurate, reliable, secure, compliant and aligned with business and client expectations. Combines traditional QA practices with AI / ML validation techniques to test data integrity, model performance, automation workflows and user outcomes. Works with engineering, data science, product management, cybersecurity, legal and risk teams to validate AI-enabled products and operational processes. Continuously improves testing frameworks, monitoring methodologies, governance standards and automation capabilities to support scalable and trustworthy AI adoption.

Leads testing efforts for moderately complex AI products, features and platform enhancements. Designs advanced test plans covering model accuracy, bias detection, explainability and operational resilience. Develops automated testing frameworks and monitoring approaches for AI systems. Performs detailed analysis of defects, model drift and production quality issues. Partners with cross-functional stakeholders to define acceptance criteria and quality standards. Mentors junior analysts and reviews testing deliverables for quality and consistency. Supports implementation of enterprise AI governance and risk management controls. Recommends improvements to testing methodologies, tooling and operational processes. Facilitates quality reviews, stakeholder workshops, model validation discussions, and testing strategy sessions. Identifies operational, technical, compliance, and model-related risks and recommends mitigation strategies. Leads validation activities for AI models, data pipelines, automation workflows, user-facing AI capabilities, vendors, tools, and third-party technologies. Evaluates quality, reliability, security, governance, and compliance considerations associated with AI products and services.