AI Solutions Engineer, Manufacturing

Position Summary

• The AI Solutions Engineer is a handson shopfloor engineer who applies AI and dataenabled tools to improve safety quality throughput productivity and timetoproficiency in skilled labor roles. This role partners directly with Operations Quality Maintenance EHS Supply Chain and Training to translate real manufacturing problems into practical scalable AI applications that are adopted and sustained on the floor.
• The role is a Manufacturing Engineer / AI Engineer hybrid spending approximately 75% of time developing and maintaining plant AI applications and 25% of time in manufacturing working alongside operators supervisors and maintenance teams to ensure solutions reflect real process behavior and are embedded into standard work.

Key Responsibilities

Plant AI Enablement & Manufacturing Partnership

• Partner with Operations Quality Maintenance EHS Supply Chain and Training to identify and prioritize highvalue AI use cases tied to safety quality throughput productivity and workforce capability.
• Build and manage a plant AI opportunity pipeline including use cases value hypotheses owners required data timing and success metrics.
• Define clear problem statements requirements and KPIs (e.g. defect escape reduction downtime reduction cycle time improvement injury risk reduction faster time to proficiency).
• Lead pilots from concept through shopfloor adoption including data readiness trial design operator input training launch and sustainment.
• Ensure AI solutions are simple explainable and usable for operators and supervisors integrated into standard work and leader routines.
• Identify and mitigate operational and safety risks (failure modes false positives/negatives bias safety impacts) and ensure controls and escalation paths are in place.

Manufacturing & Process Engineering

• Improve manufacturing processes across machining forming assembly and inspection operations.
• Lead root cause analysis related to scrap rework downtime delinquencies trainingrelated errors and safety risks.
• Develop improve and sustain standard work process flows layouts tooling and capability studies.
• Support equipment commissioning process optimization and reliability improvement in partnership with Maintenance and Operations.

AI Application Development (PlantIT Collaboration)

• Own handson development deployment and sustainment of lightweight AIenabled plant applications (prototypes through targeted production features) using Division and Corporate IT/AI standards for architecture security and governance.
• Serve as the manufacturing product owner for plant AI applications by defining requirements validating outputs against shopfloor reality and ensuring usability for end users.
• Lead the endtoend lifecycle for plant AI solutions (design development testing release training sustainment) and escalate design decisions and risks as needed.
• Develop and maintain solutions such as Databricks Apps internal dashboards decision tools and AIassisted workflows that operationalize manufacturing use cases.
• Integrate APIs model endpoints and data services into userfacing tools; document assumptions controls and escalation paths.
• Use Git and follow agreed release testing and changemanagement practices; provide Tier 1 support and coordinate enhancements with IT and Corporate AI teams.

Workforce Capability & TimetoProficiency Improvement

• Apply AIenabled training and development tools to reduce time to proficiency in key skilled labor roles (e.g. machinists thread roll operators maintenance technicians inspectors).
• Partner with Engineering Operations and Training to identify skill gaps higherror steps and learning friction points.
• Develop AIsupported tools such as digital work instructions visual job aids troubleshooting assistants and skillprogression checkpoints.
• Enable supervisors and trainers with datadriven insights to focus coaching standardize training across shifts and reinforce correct behaviors.
• Validate training effectiveness through faster readiness for independent work reduced earlystage defects and downtime and improved retention.

Data Continuous Improvement & Change Leadership

• Use MES SPC quality systems downtime tracking sensor/PLC data and learning data to drive improvement.
• Validate AI outputs through handson observation and process confirmation.
• Establish KPIs control plans and visual management to sustain gains.
• Act as a change agent by training and coaching operators and supervisors and embedding improvements into standard work and leader routines.

Reporting & Governance

• Primary accountability is to plant leadership for operational results and performance improvement.
• Dottedline alignment with Division IT to ensure consistency with enterprise AI strategy security and standards.
• Prepare and deliver quarterly updates to crossfunctional leadership (Division Corporate AI IT Quality Operations HR and Site Leadership).

Qualifications & Skills

Education / Experience

• Bachelors degree in Manufacturing Engineering Mechanical Engineering Computer Science or related field; experience in a highmix / highvolume manufacturing environment strongly preferred.

Technical & Functional

• Experience building AI agents AI assistants LLM-powered workflows RAG applications or similar AI systems strongly preferred.

• Applied AI and digital fluency with light coding capability; able to build prototypes and small production applications.

• Proficiency with Python or similar scripting; familiarity with simple UIs (Streamlit/Dashstyle) and REST APIs.
• Working knowledge of MES SPC quality systems downtime systems and industrial data sources.
• Familiarity with Git and basic software delivery practices.
• Strong manufacturing engineering fundamentals (process capability variation reduction PFMEA/control plans standard work).
• Proven structured problem solving (5 Why fishbone DOE where appropriate) and Lean/CI leadership.

Leadership & Communication

• Strong crossfunctional partnering skills; influences without authority.
• Effective communicator with hourly teams and leaders.
• Demonstrated changemanagement capability and safety mindset.

What Success Looks Like

• Sustained reductions in scrap rework downtime and trainingrelated errors.
• Faster time to proficiency in skilled labor roles.
• AI applications that are used daily and embedded in standard work.
• Measurable improvements in safety quality delivery and cost.
• Manufacturing teams that trust and rely on datadriven insights.