This role builds, deploys, and maintains production-grade machine learning (ML) models that solve high-value manufacturing challenges such as predictive maintenance, anomaly detection, quality inspection, and process optimization. This role develops robust data pipelines from factory sensors and systems, implements MLOps practices for reliable model monitoring and retraining, and integrates AI solutions with existing controls and process infrastructure. The AI Engineer collaborates cross-functionally to turn business problems into scalable, explainable AI applications that deliver measurable operational impact.

Job Roles 

• Data-informed consultant -- listens well to understand processes and critical performance aspects, then provides data-informed insights and recommendations; maintains focus as an internal partner versus outside observer; balances predictive insights with problem solving and allocates time (both to projects and globally) accordingly
• Strategy support -- understands plant, campus, segment and enterprise strategy and prioritizes efforts accordingly, quantifies prospective interventions using common metrics; uses analytics and modeling to improve stakeholders’ ability to identify and achieve toward established hoshin
• Forecast oriented -- comprehends how both project and system-oriented decisions and interventions scale into the future; analyzes trends, anticipates business needs based on partner input and prioritizes focal points accordingly
• Persuasion and influence  -- is objectively persuasive based on data, and interpersonally persuasive based on the ability to create trust and emphasize common goals; overcomes skepticism and invites hypothesis testing; understands the culture and how to accomplish things within it, yet has the courage to challenge existing methods
• System creation -- standardizes data use to create structure and systems of information and access; makes recommendations that, over time, alter existing systems or create new systems
• Measurement implementation -- collaborates with stakeholders to create measurements for interventions; tracks effectiveness as a guiding standard; learns information and outcomes that might initially be unfamiliar or outside incumbent expertise; maximizes and proves returns on investment at local and global levels
• Builder/creator -- has an entrepreneurial spirit to build a position/department/practice that grows and provides increasing benefits over time; enjoys the challenge of creating possibilities and proving concepts; believes in the prospective benefits and wants to scale them
• Translator and storyteller -- acts as liaison between information and application; synthesizes information and conveys it in the best form for stakeholder understanding and use; creates narratives from numbers, creating clear and engaging ideal state scenarios toward which the enterprise can strive 
• Complexity comfort -- manages the tension between what we know and what we don’t know, between what we can implement now and what we must implement later; understands the breadth and depth of available information, yet introduces it to others in discerned measures, always toward practical and aspirational outcomes; transitions between global and local focus with ease; prioritizes continuous learning, technological and marketplace awareness to remain informed and help educate the organization
• Interpersonally adept --  builds and maintains relationships at varying levels throughout the organization; inspires trust and connection based on objectivity, expertise and curiosity; collaborates cross functionally
• Develop and deploy machine learning models for manufacturing use cases (predictive maintenance, quality inspection, process optimization, demand forecasting).
• Build and manage data pipelines from factory sensors/IoT/ERP systems; perform feature engineering and model training.
• Implement MLOps practices: containerization, monitoring, retraining, and scaling AI in edge/cloud environments.
• Collaborate cross-functionally to translate business problems into AI solutions and integrate models with controls/process systems.
• Monitor model performance, ensure explainability/ethics, and drive continuous improvement.

Basic Qualifications

• BS in Computer Science, Data Science, or Engineering from an accredited institution

• Employees must be legally authorized to work in the United States.  Verification of employment eligibility will be required at the time of hire.  Visa sponsorship is not available for this position.

   

Preferred Qualifications

• 2+ years AI/ML experience
• Strong Python, TensorFlow/PyTorch, cloud (AWS/Azure) experience
• MS in Computer Science, Data Science, or Engineering from an accredited institution