JobsGenerac

AI Application Engineer

Generac · Waukesha, WI

Posted Sep 1, 2026 · We last checked this listing on Sep 20, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

Behavioral

Tell me about a time you had to translate a vague operations complaint, like 'yield is off on line 3,' into a specific AI or data problem you could actually solve.

ability to bridge business language and technical scoping, which is central to this role

Tell me about a project where the model itself worked fine, but adoption on the floor failed anyway. What happened?

understanding that deployment success depends on people, not just accuracy metrics

Tell me about a time you had to explain a technical tradeoff, like model accuracy versus latency, to a leadership audience with no ML background.

communication skill across technical and executive audiences

Describe a time you had to work across operations, IT, and engineering teams that didn't agree on priorities. How did you get everyone moving in the same direction?

cross-functional collaboration skill in an environment with competing stakeholder interests

Technical

Walk me through an AI vision system you built and deployed on a production line. What was the use case, and how did you know it was actually working once it was live?

whether the candidate has real deployment experience versus just model-building experience

How would you connect a predictive maintenance model to data coming off a PLC or SCADA system? What does that integration path look like end to end?

real familiarity with OT systems and how AI actually gets fed shop floor data

What's your process for deciding whether to build a model in-house or buy an existing solution for a manufacturing use case?

business judgment and awareness of cost/time tradeoffs beyond pure technical interest

How have you set up a data pipeline that pulls from an enterprise system like SAP or MES and feeds it into a model? What broke, and how did you fix it?

hands-on data engineering experience versus theoretical knowledge of ETL

What does model governance look like in a manufacturing environment where several sites might use variations of the same model?

awareness of standardization, versioning, and monitoring at scale across sites

How would you approach deploying a model on edge hardware on the shop floor versus running it in the cloud, and what would push you toward one or the other?

depth of knowledge on edge AI and practical constraints of factory environments

Once a model is in production, how do you monitor it over time, and what tells you it's starting to drift or degrade?

discipline around ongoing model maintenance, not just initial deployment

Situational

Say you have a quality inspection model that was 95 percent accurate in testing but is flagging good parts as defective on the floor. Walk me through how you'd troubleshoot that.

practical debugging instincts for models in production, not just theoretical ML knowledge

Imagine a plant manager tells you the AI throughput tool you built is 'just noise' and won't use it. What do you do next?

ability to handle resistance and rebuild trust with a non-technical stakeholder

If you joined and had to pick your first AI use case here at Generac, what would you look for to decide it was worth doing first?

prioritization instinct and understanding of what makes a manufacturing AI project actually deployable and valuable

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The full job description

As published by Generac.

We believe power is a promise - a shared commitment to be there for others when it matters most. For more than 65 years, we've turned big ideas into solutions that help protect homes, strengthen businesses and build a more resilient, efficient, sustainable energy future. Ready to Power a Smarter World with us? The AI Applications Engineer will design, develop, and deploy scalable AI/ML solutions that accelerate digital transformation across manufacturing operations. This role will focus on translating operational challenges into deployable AI solutions—integrating operations platforms to enable smarter decision-making, automation, and predictive insights. This position plays a critical role in building the “Digital Factory + AI” capability stack. This position could include up to 25% travel locally.  Major Responsibilities:  AI Solution Development & Deployment   • Design, build, test, and deploy machine learning and AI models, including productionizing solutions and maintaining model performance over time.   Manufacturing (Operations) Use Case Delivery   • Develop and support AI solutions for shop floor applications such as predictive maintenance, quality inspection, yield optimization, and throughput improvement.  Data Engineering & Platform Integration   • Develop data pipelines, integrate with enterprise systems, and ensure scalable, reusable AI and data architecture.  Cross-Functional Collaboration & Business Translation   • Partner with Operations and IT teams to define use cases, translate business problems into AI solutions, and ensure adoption. Including build vs. buy analysis.    Continuous Improvement, Governance & Documentation   • Monitor model performance, ensure data/model governance, document solutions, and drive reusability and standardization across sites/functions.  Minimum Job Requirements: Education  • Bachelor’s degree in computer science, engineering, data science, or related field   Work Experience  • Experience working with structured and unstructured data   • Experience building and deploying AI based vision systems  • 5 years in manufacturing / OT environments, PLC, SCADA, MES exposure  • 2 years of experience in AI/ML development and deployment      Knowledge / Skills / Abilities  • Python (TensorFlow, PyTorch, Scikit-learn)  • Data engineering and ETL pipelines  • Model deployment (APIs, microservices, Docker, etc.)  • Understanding of industrial systems, manufacturing processes, or IoT data  • Strong problem-solving and systems thinking mindset   • Ability to bridge technical and business domains   • Execution-focused with a bias toward deployment (not just modeling)   • Ability to work across operations, engineering, and service functions   • Ability to demonstrate clear communication with both technical teams and leadership  Preferred Job Requirements:  Education  • Master’s Degree  Work Experience  • Experience in discrete manufacturing environments   • Experience working in digital factory or Industry 4.0 initiatives  • Familiarity with MES, PLM, and ERP integrations (SAP, Tulip, Windchill, etc.)    Knowledge / Skills / Abilities  • Experience with Computer vision (OpenCV, vision models  • Time-series analysis (sensor, telemetry data)  • Edge AI deployment  “We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, national origin, disability status, protected veteran status, or any other characteristic protected by law.”

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