Lead AI Quality Assurance Engineer

Primary work location: Berwyn, Raleigh, Boston, Chicago, or Seattle with a hybrid work model.

About Envestnet

Envestnet is an adaptive WealthTech company that is redefining the future of wealth management by helping advisors meet the moment with its comprehensive technology, actionable insights, and industry leading support. Backed by over 25 years of experience and approximately $7.0 trillion in platform assets, Envestnet is trusted by over one third of financial advisors across leading banks, wealth managers, brokerages, and RIAs. For a deeper look at how Envestnet is shaping the future of financial advice, visit www.envestnet.com.

The Team You’ll Join

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.

Responsibilities

• 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.

Requirements

• Ability to evaluate complex problems, identify root causes, assess alternatives, and implement practical, scalable, data-driven solutions.
• Demonstrated ability to establish and maintain productive relationships across business, technology, product, engineering, data science, and risk organizations.
• Knowledge of process improvement techniques, operational workflows, dependency management, quality optimization, and governance practices.
• Ability to communicate technical and non-technical concepts clearly to diverse audiences, including leadership stakeholders.
• Experience coordinating cross-functional initiatives, managing dependencies, tracking progress, and delivering measurable outcomes.
• Strong understanding of AI concepts, machine learning fundamentals, AI system capabilities, limitations, risks, and practical applications, with experience validating AI/ML models, Generative AI applications, LLM-enabled systems, or data science solutions.
• Experience with automated testing frameworks, API testing, model evaluation, prompt testing, hallucination testing, data quality validation, AI governance controls, and regulatory, security, privacy, or responsible AI requirements.

Benefits

• Medical insurance
• Paid time off (PTO)
• 401k company match
• Paid parental leave
• Education reimbursement
• Disability coverage
• Mental health & wellness support

Pay

This role offers a base salary range of $152,300 to $190,400. The range listed represents a good-faith estimate of base salary compensation for this position and does not include incentive compensation, equity or benefits. Individual pay will be determined based on factors including, but not limited to, relevant experience, skills, education, certifications, and geographic location, in accordance with applicable pay transparency laws. This role is eligible for an additional incentive component as part of the total rewards package.