AI Engineer Manufacturing Plant AI Applications

PCC Talent Acquisition Portal•Santa Ana, CA

1d•Onsite

About The Position

The AI Engineer is a hands‑on, shop‑floor engineer who applies AI and data‑enabled tools to improve safety, quality, throughput, productivity, and time‑to‑proficiency 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 50–60% of time developing and maintaining plant AI applications and 40–50% of time in manufacturing, working alongside operators, supervisors, and maintenance teams to ensure solutions reflect real process behavior and are embedded into standard work.

Requirements

Bachelor’s degree in Manufacturing Engineering, Mechanical Engineering, Computer Science, or related field
Experience in a high‑mix / high‑volume manufacturing environment strongly preferred
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
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/Dash‑style) 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 cross‑functional partnering skills; influences without authority
Effective communicator with hourly teams and leaders
Demonstrated change‑management capability and safety mindset

Responsibilities

Partner with Operations, Quality, Maintenance, EHS, Supply Chain, and Training to identify and prioritize high‑value 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 shop‑floor 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.
Improve manufacturing processes across machining, forming, assembly, and inspection operations.
Lead root cause analysis related to scrap, rework, downtime, delinquencies, training‑related 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.
Own hands‑on development, deployment, and sustainment of lightweight AI‑enabled 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 shop‑floor reality, and ensuring usability for end users.
Lead the end‑to‑end 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 AI‑assisted workflows that operationalize manufacturing use cases.
Integrate APIs, model endpoints, and data services into user‑facing tools; document assumptions, controls, and escalation paths.
Use Git and follow agreed release, testing, and change‑management practices; provide Tier 1 support and coordinate enhancements with IT and Corporate AI teams.
Apply AI‑enabled 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, high‑error steps, and learning friction points.
Develop AI‑supported tools such as digital work instructions, visual job aids, troubleshooting assistants, and skill‑progression checkpoints.
Enable supervisors and trainers with data‑driven insights to focus coaching, standardize training across shifts, and reinforce correct behaviors.
Validate training effectiveness through faster readiness for independent work, reduced early‑stage defects and downtime, and improved retention.
Use MES, SPC, quality systems, downtime tracking, sensor/PLC data, and learning data to drive improvement.
Validate AI outputs through hands‑on 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.
Prepare and deliver quarterly updates to cross‑functional leadership (Division, Corporate AI, IT, Quality, Operations, HR, and Site Leadership).