AI Manufacturing Engineer

Itron•Raleigh, SC

9d•Hybrid

About The Position

The AI Manufacturing Engineer designs, develops, deploys, and supports AI-enabled solutions that enhance manufacturing processes, test systems, product quality, and factory operations. This role combines expertise in industrial engineering, data science, machine learning, software development, and manufacturing systems to solve complex operational challenges in a technology-driven Utilities & Energy environment. The ideal candidate is passionate about applying artificial intelligence technologies to real-world manufacturing environments and can translate business needs into practical, scalable solutions that improve operational performance, customer outcomes, and business value. The successful candidate demonstrates a customer-centric mindset, accountability for results, and the ability to collaborate effectively across cross-functional teams.

Requirements

Bachelor’s degree in Computer Science, Electrical Engineering, Industrial Engineering, Data Science, Manufacturing Engineering, or a related technical field.
Experience developing solutions using Python or similar programming languages.
Experience with MLOps, model deployment, monitoring, and lifecycle management.
Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-Learn, or equivalent technologies.
Experience with data analysis, statistics, predictive modeling, and advanced analytics techniques.
Experience working with SQL databases, data processing tools, and large-scale datasets.
Understanding of manufacturing processes, industrial automation, quality systems, operational environments, or related production systems.
Experience in manufacturing engineering, industrial engineering, test engineering, quality engineering, operations engineering, or related disciplines.
Experience implementing AI agents and intelligent automation solutions for manufacturing and quality process improvements.
Experience developing dashboards, reporting tools, and data visualization solutions.
Strong problem-solving, analytical thinking, and root-cause investigation skills.
Ability to communicate complex technical concepts clearly and effectively to diverse audiences.
Ability to work collaboratively across multiple functions, teams, and geographic locations.
Strong project management, execution, and organizational skills.
Demonstrated ability to deliver customer-focused solutions that drive operational improvements and business results.
Commitment to continuous learning, innovation, accountability, and inclusive collaboration.

Nice To Haves

Experience with computer vision, industrial image processing, and automated inspection technologies.
Experience with cloud platforms such as Microsoft Azure and cloud-based AI services.
Knowledge of Industrial Internet of Things (IIoT), MES, SCADA, PLCs, industrial control systems, and manufacturing data platforms.
Experience deploying AI and machine learning solutions in production manufacturing environments.
Familiarity with Statistical Process Control (SPC), Six Sigma, Lean Manufacturing, Kaizen, or continuous improvement methodologies.
Knowledge of electronics manufacturing, utility products, smart metering technologies, test systems, industrial equipment, or energy-focused manufacturing operations.
Experience working with digital twin technologies, simulation tools, or advanced manufacturing analytics platforms.
Familiarity with data governance, responsible AI practices, cybersecurity principles, and regulatory compliance requirements.
Experience supporting smart factory, connected factory, or Industry 4.0 transformation initiatives.
Advanced degree in a relevant engineering, computer science, data science, or analytics discipline.

Responsibilities

Develop, train, validate, and deploy machine learning models for manufacturing applications.
Build predictive analytics solutions that improve product quality, throughput, equipment reliability, process optimization, and customer outcomes.
Design AI-based systems for anomaly detection, defect classification, root-cause analysis, and predictive maintenance.
Develop computer vision solutions for automated inspection, quality assurance, and manufacturing process verification.
Collaborate with manufacturing, quality, operations, and test engineering subject matter experts to identify high-value AI use cases and prioritize implementation opportunities.
Support Industry 4.0 initiatives through the deployment of AI-enabled manufacturing solutions that drive operational excellence and innovation.
Integrate AI applications with Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP) platforms, Product Lifecycle Management (PLM) systems, manufacturing databases, test systems, and production equipment.
Create dashboards, monitoring tools, and decision-support systems that transform manufacturing data into actionable insights.
Implement digital twins and process simulation capabilities to improve manufacturing efficiency and decision-making.
Develop scalable data pipelines for structured and unstructured manufacturing data from production, quality, and operational systems.
Ensure model accuracy, reproducibility, data integrity, cybersecurity requirements, and regulatory compliance.
Establish and maintain best practices for AI governance, model monitoring, lifecycle management, and responsible AI deployment.
Partner with Manufacturing Engineering teams to improve production yields, cycle times, Overall Equipment Effectiveness (OEE), capacity utilization, and process stability.
Support New Product Introduction (NPI) activities through data-driven manufacturing analytics and process optimization.
Collaborate with Test Engineering teams to improve automated test coverage, diagnostic capability, and test efficiency.
Participate in continuous improvement initiatives using AI-driven insights and data-informed decision making.
Work cross-functionally with Operations, Quality, Product Development, Supply Chain, Information Technology, and other stakeholders to deliver business results.
Communicate technical findings, recommendations, and project outcomes effectively to both technical and non-technical audiences.
Provide guidance, training, and mentorship on AI technologies, advanced analytics, and data-driven decision-making practices.
Stay current on emerging AI technologies, manufacturing innovations, industry trends, and best practices.
Demonstrate accountability for project execution, deliverables, and measurable business impact while contributing to a culture of agility, collaboration, authenticity, and innovation.