Senior AI/ML Engineer for Utilities & Grid Analytics

EY; Houston (TX); Hybrid; USD 126,000 - 230,000; Full time

Benefits offered by this job

Hybrid work model

Competitive compensation

Paid time off

Job summary

EY in the United States seeks a Manager in AI Native Engineering to lead client-facing data science and big data initiatives across regulated utilities contexts. You will guide teams, design scalable AI/ML solutions, and translate insights into business value while collaborating with IT, operations, and client executives.

Expect hybrid work, cross-border collaboration, and an emphasis on governance, risk, and regulatory considerations as you move AI solutions from pilots to production within

Qualifications

• Bachelor’s degree in a relevant field.
• 6–10 years of AI/Data Science/Machine Learning experience.
• 2–4 years of direct management of technical teams.
• Strong Python and cloud tech experience.
• Experience delivering production AI/ML solutions on a major cloud platform.
• Excellent communication and collaboration across hybrid/global teams.

Responsibilities

• Lead workstreams delivering complex AI/ML initiatives for utility clients.
• Design scalable data science solutions across data sources and platforms.
• Guide teams and transfer AI/analytics outputs into business insights.
• Engage with clients, manage engagements, and drive project outcomes.
• Ensure governance, risk management, and regulatory alignment in AI delivery.

Skills

Python

Generative AI

Machine Learning

Cloud platforms

DevOps

SQL

Communication

Education

Bachelor’s degree

Tools

OpenAI

DALL-E

Langchain

PyTorch

scikit-learn

Azure DevOps

Job description

EY in the United States seeks a Manager in AI Native Engineering to lead client-facing data science and big data initiatives across regulated utilities contexts. You will guide teams, design scalable AI/ML solutions, and translate insights into business value while collaborating with IT, operations, and client executives.

Expect hybrid work, cross-border collaboration, and an emphasis on governance, risk, and regulatory considerations as you move AI solutions from pilots to production within