US Tech - AI Evaluation Engineer (QA) Manager

PwC•Charlotte, NC

About The Position

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems. Enhancing your leadership style, you motivate, develop and inspire others to deliver quality. You are responsible for coaching, leveraging team member’s unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important role in identifying opportunities that contribute to the success of our Firm. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same. Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to: Analyse and identify the linkages and interactions between the component parts of an entire system. Take ownership of projects, ensuring their successful planning, budgeting, execution, and completion. Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables. Develop skills outside your comfort zone, and encourage others to do the same. Effectively mentor others. Use the review of work as an opportunity to deepen the expertise of team members. Address conflicts or issues, engaging in difficult conversations with clients, team members and other stakeholders, escalating where appropriate. Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements. The Opportunity As part of the People Tech & AI team you will lead teams delivering governed Generative AI solutions, designing testing strategies, evaluation frameworks, and governance controls to promote reliable, ethical, and scalable AI agents. As a Manager you will supervise teams, manage client accounts, and apply leading practices across AI platforms, data pipelines, and integrations, while upholding PwC’s values and professional standards. This role offers the chance to drive innovation in AI technology while fostering a collaborative environment that empowers team members and enhances client relationships.

Requirements

Bachelor's Degree
At least 6 years of experience in AI/ML engineering, data engineering, or software engineering
In lieu of a Bachelor's Degree, demonstrating, in addition to the minimum years of experience required for the role, three years of specialized training and/or progressively responsible work experience in technology for each missing year of college

Nice To Haves

Master's Degree preferred
Proven experience leading AI solution teams
Demonstrating understanding of LLMs and agent architectures
Possessing experience with Microsoft Power Platform
Bringing prior consulting or professional services experience
Establishing testing and validation standards for AI agents
Managing and coaching teams for continuous development
Building trusted client relationships and translating requirements
Designing and overseeing automated testing frameworks for Microsoft Copilot Studio and Google AgentSpace, including unit, integration, end-to-end, and regression testing, as well as low-code workflows, connectors, plugins, and grounding data
Leading governance of end-to-end AI agent pipelines and data flows, verifying integration correctness and resiliency, data quality, lineage, transformation accuracy, and embedded automated quality gates across CI/CD pipelines