Tax Innovation - AI Engineer - Manager

PricewaterhouseCoopers•Dallas, TX

About The Position

As a Tax Innovation - Artificial Intelligence Engineer, you will lead the design, development, and deployment of AI solutions to enhance performance and capabilities within our Tax practice. You will oversee client engagement workstreams, guide multidisciplinary teams, and translate complex business needs into scalable, production-ready AI solutions. As a Manager, you will leverage your technical and leadership skills to mentor junior staff, manage projects, and navigate ambiguity while upholding PwC's quality standards. In this role at PwC, you will focus on building AI solutions using foundation models, machine learning, agentic systems, retrieval technologies, and modern software engineering practices. You will guide teams through solution design, experimentation, integration, deployment, and ongoing optimization while addressing security, compliance, data quality, and auditability requirements. Your intellectual curiosity and ability to embrace technology and innovation will help drive quality delivery and identify new opportunities for our clients and firm.

Requirements

At least a Bachelor's degree
At least 4 years of experience

Nice To Haves

Preference for at least one of the following fields of study: Analytics/Data Science, Artificial Intelligence/Robotics, Computer Science/Information Systems, Engineering
At least one of the following: Certifications aligned to artificial intelligence, machine learning, data engineering, or cloud platforms, including AWS, Google Cloud, Microsoft Azure, Databricks, Snowflake, or related data and AI credentials
Demonstrating proficiency in AI implementation, Generative AI, and Machine Learning
Applying foundation models, Natural Language Processing, and Retrieval-Augmented Generation techniques
Utilizing Python to develop AI applications and integrate APIs
Developing agentic solutions and orchestration workflows
Applying data integration, modeling, and retrieval techniques to enterprise AI solutions
Implementing MLOps and LLMOps practices for production AI systems
Applying security, Responsible AI, compliance, and data quality requirements
Leading AI engineering workstreams and coaching teams through solution delivery

Responsibilities

Leading the design, development, and deployment of scalable AI and GenAI solutions
Translating business requirements into secure, production-ready AI architectures and applications
Applying foundation models, machine learning, natural language processing, retrieval, and agentic technologies to solution design
Leading experimentation and evaluation to validate solution feasibility, quality, and performance
Designing data, knowledge, and retrieval solutions, including data integration, document processing, vector search, and retrieval-augmented generation
Developing AI applications using Python, APIs, and modern software engineering practices
Implementing agentic workflows, orchestration patterns, and human-in-the-loop controls
Applying MLOps and LLMOps practices to support deployment, monitoring, quality measurement, and optimization
Confirming AI solutions meet security, privacy, compliance, Responsible AI, data quality, and auditability requirements
Collaborating with stakeholders to identify business and data challenges and refine solutions
Managing project workstreams and guiding teams through complex delivery challenges
Coaching and providing feedback to team members to support skill development and performance