Artificial Intelligence (AI) Engineer

Precision Castparts; Jenkintown (PA); On-site; USD 120,000 - 180,000; Full time

Job summary

Precision Castparts seeks an AI Engineer to apply AI and data-enabled tools on the shop floor to improve safety, quality, throughput, and time-to-proficiency in skilled labor roles. You will partner with Engineering, Quality, IT, Maintenance, EHS, Supply Chain, and HR to translate manufacturing problems into scalable AI solutions adopted in standard work.

The role blends manufacturing and AI development, spending about half the time building and maintaining plant AI applications and half in

Qualifications

• Familiarity with Git and software delivery practices.
• Applied AI and digital fluency with light coding; build prototypes.
• Proficiency with Python or similar scripting; basic UIs and REST APIs.

Responsibilities

• Identify high-value AI use cases with cross-functional partners.
• Build and manage a plant AI opportunity pipeline with data needs and KPIs.
• Lead pilots from concept to shop-floor adoption with training and deployment.
• Ensure AI solutions are simple, explainable, and usable by operators and leaders.
• Integrate AI tools with standard work and improve maintenance, safety, and quality metrics.
• Develop and maintain lightweight plant AI apps and dashboards.

Skills

Git basics

AI literacy

Python scripting

UI basics

Education

Bachelor’s degree in Manufacturing/Engineering/CS

Tools

Databricks

Job description

Position Summary

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 Engineering, Quality, IT, Maintenance, EHS, Supply Chain, and HR to translate real manufacturing problems into practical, scalable AI applications that are adopted and sustained on the floor.

Position Summary

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 Engineering, Quality, IT, Maintenance, EHS, Supply Chain, and HR 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.

Key Responsibilities

Plant AI Enablement & Manufacturing Partnership

• Partner with Engineering, Quality, Maintenance, EHS, Supply Chain, and HR 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.

Manufacturing & Process Engineering

• 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.

AI Application Development (Plant–IT Collaboration)

• 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 elevate 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.

Workforce Capability & Time‑to‑Proficiency Improvement

• 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 HR 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.

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 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.

Required Qualifications

Experience & Education

• Familiarity with Git and basic software delivery practices.
• 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.

Preferred Qualifications

• 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.
• Experience with CNC, forming, or precision manufacturing equipment
• Working knowledge of MES, SPC, quality systems, downtime systems, and industrial data sources.
• Familiarity with aerospace, automotive, medical or other highly regulated manufacturing fields

Education

• Bachelor’s degree in Manufacturing Engineering, Mechanical Engineering, Computer Science, or related field; experience in a high‑mix / high‑volume manufacturing environment strongly preferred.