AI Engineering Manager — GenAI & MLOps Leader

PwC; Dallas (TX); On-site; USD 99,000 - 266,000; Full time

Benefits offered by this job

Medical insurance

401k plan

Paid time off

Job summary

PwC in the United States is seeking a Tax Innovation - Artificial Intelligence Engineer at Manager level to lead the design, development, and deployment of AI solutions within the Tax practice. You will oversee client engagement workstreams, mentor staff, and translate complex business needs into production-ready AI systems.

You will apply foundation models, ML, NLP, retrieval, and agentic technologies; implement MLOps/LLMOps; ensure security, privacy, data quality, and auditability, while

Qualifications

• Bachelor's degree required.
• 4+ years of relevant experience.

Responsibilities

• Lead design, development, and deployment of scalable AI solutions.
• Translate business needs into secure, production-ready AI architectures.
• Apply foundation models, ML, NLP, retrieval, and agentic technologies.
• Lead experimentation, evaluation, and solution optimization.
• Design data, knowledge, and retrieval solutions and orchestrate AI workflows.
• Develop AI applications using Python, APIs, and modern software practices.
• Implement MLOps/LLMOps and ensure security, privacy, and auditability.
• Coach and guide teams; manage project workstreams and stakeholder engagement.

Skills

AI Engineering Leadership

GenAI

ML & NLP

Solution Architecture

Project Management

Leadership & Mentoring

Education

Bachelor's degree

Tools

Python

APIs

Foundation Models

NLP

Retrieval-Augmented Generation

LLMs

AWS

GCP

Azure

Databricks

Snowflake

Job description

PwC in the United States is seeking a Tax Innovation - Artificial Intelligence Engineer at Manager level to lead the design, development, and deployment of AI solutions within the Tax practice. You will oversee client engagement workstreams, mentor staff, and translate complex business needs into production-ready AI systems.

You will apply foundation models, ML, NLP, retrieval, and agentic technologies; implement MLOps/LLMOps; ensure security, privacy, data quality, and auditability, while