AI/ML Integration Engineer – Battery Plant

Ford Energy; Glendale (KY); Hybrid; USD 133,000 - 251,000; Full time

Job summary

Ford Energy, a Ford Motor Company subsidiary, seeks a data-driven engineer to drive yield, quality, and throughput improvements on the manufacturing floor across plants. You will build and deploy models on plant data, connect MES and PLC sources, and translate insights into actionable process changes with cross-functional teams.

Requirements include a Bachelor’s in Engineering and experience with Python and analytics on industrial data; emphasis on high-volume manufacturing and reliability.

Qualifications

• Bachelor’s degree in Mechanical, Industrial or Electrical Engineering with ML/analytics coursework or experience.
• Strong plant-floor instinct for identifying losses and optimization opportunities.
• Proficiency with Python and plant data systems (MES, historians such as PI or Ignition, PLC data).

Responsibilities

• Work the line day to day: identify losses (scrap, downtime, quality escapes) and apply AI/analytics to address them.
• Build and deploy models on plant data for coating and formation quality, defect detection, and yield root cause.
• Translate model output into action: adjust parameters, implement error-proofing, and establish standard work with process controls and quality.
• Share findings and reusable assets with the central technologist so both plants benefit.
• Own the local data connection: MES, historians, PLC and OT data, and inline metrology.

Skills

Python

ML/data analytics

Plant-floor intuition

Process optimization

Education

Bachelor’s degree in Mechanical/Industrial/Electrical Engineering

Tools

MES systems

PI Historian

Ignition

PLC data

Job description

Ford Energy, a Ford Motor Company subsidiary, seeks a data-driven engineer to drive yield, quality, and throughput improvements on the manufacturing floor across plants. You will build and deploy models on plant data, connect MES and PLC sources, and translate insights into actionable process changes with cross-functional teams.

Requirements include a Bachelor’s in Engineering and experience with Python and analytics on industrial data; emphasis on high-volume manufacturing and reliability.