AI Manufacturing Engineer

We need to output HTML fragment with sections: headings

for each section present: About the role, Responsibilities, Requirements (or Skills?), Benefits, maybe Pay/Schedule only if present. The posting includes "Benefits Info" paragraph. It mentions "hybrid work schedule" but not explicit schedule details. Should we include Schedule? The rule: never create Pay/Schedule/Benefits sections unless posting actually states them. Benefits is stated: "Benefits Info This position also includes a competitive benefit package including; financial, social, health and wellbeing programs, paid vacation, 401k matching, employee stock purchase program, hybrid work schedule, and more!" So we can have Benefits section. Schedule: they mention hybrid work schedule as part of benefits, but not a separate schedule section. Probably not needed. We need sections: About the role (intro paragraph), Responsibilities (list of duties), Required Skills & Experience (maybe as Requirements or Skills), Preferred Skills & Experience (maybe as Preferred Qualifications), Benefits. We must not repeat job title. Use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We must preserve all substantive information, not invent or summarize away detail. Remove boilerplate: cookie/navigation/legal/EEO/equal-opportunity/scam-warning, duplicated lines, application instructions. The posting includes EEO statements and accommodation contact, also "Itron is committed to building an inclusive..." and "If you are excited about this role but your past experiences don't perfectly align..." and "The successful candidate's starting wage will be determined..." and "Itron is proud to be an Equal Opportunity Employer." Also "If you require an accommodation...". Also final paragraph about Itron transforming... and "Join us as we create a more resourceful world: www.itron.com". Those are boilerplate/legal/EEO etc. Should be removed. Also "Benefits Info" heading? We'll make

  Benefits

  and then a
  • list of benefits. We need to keep the description of role: "Itron is innovating new ways for utilities and cities to manage energy and water. We create a more resourceful world to protect essential resources for today and tomorrow. Join us. 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." That's the About the role prose. Then Duties & Responsibilities list: many bullet points. We'll convert each sentence starting with a verb into
  • . Need to preserve all details. Then Required Skills & Experience: list of bullet points (each line). We'll make
    • . Then Preferred Skills & Experience: list. Then Benefits: list. We must not include headings with empty content. Ensure each section has content. We must not include markdown fences. Just raw HTML. Let's extract duties: - 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. Now Required Skills & Experience list: - 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. Preferred Skills & Experience list: - 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. Benefits list: - competitive benefit package including; financial, social, health and wellbeing programs, paid vacation, 401k matching, employee stock purchase program, hybrid work schedule, and more! We could split into items: financial programs, social programs, health and wellbeing programs, paid vacation, 401k matching, employee stock purchase program, hybrid work schedule. But the text is a single sentence. We can keep as a
      • with that sentence. Now About the role paragraph: combine the intro sentences. We need to output only HTML fragment. Let's craft:

        About the role

        Itron is innovating new ways for utilities and cities to manage energy and water. We create a more resourceful world to protect essential resources for today and tomorrow. Join us. 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.

        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.

        Required Skills & Experience

        • 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