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Generative Ai Engineer Intern Jobs in Wisconsin (NOW HIRING)

WI · On-site

$130 - $160/hr

Develop and operationalize generative AI applications leveraging large language models (LLMs ... Collaborate with data engineering teams to ensure high-quality, governed, and accessible data for ...

New

Develop and deploy Generative AI systems and LLM-powered applications (e.g., GPT, Claude, LLaMA ... Apply prompt engineering, fine-tuning, and orchestration techniques to adapt foundation models for ...

Develop and deploy Generative AI systems and LLM-powered applications (e.g., GPT, Claude, LLaMA ... Apply prompt engineering, fine-tuning, and orchestration techniques to adapt foundation models for ...

Senior AI Engineer

Middleton, WI

$107K - $147K/yr

The Sr AI Engineer is a hands-on technical leader who ships enterprise-scale AI into production and ... Build and operationalize generative AI applications using large language models (LLMs), retrieval ...

Senior AI Engineer

Middleton, WI · On-site

$107K - $147K/yr

The Sr AI Engineer is a hands-on technical leader who ships enterprise-scale AI into production and ... Build and operationalize generative AI applications using large language models (LLMs), retrieval ...

Senior AI Engineer

Middleton, WI · On-site

$107K - $147K/yr

The Sr AI Engineer is a hands-on technical leader who ships enterprise-scale AI into production and ... Build and operationalize generative AI applications using large language models (LLMs), retrieval ...

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Generative Ai Engineer Intern information

What does a generative AI engineer intern do?

A Generative AI Engineer Intern assists in developing and testing machine learning models, specifically those that can create new content such as text, images, or audio. They work with frameworks like TensorFlow or PyTorch, collaborate with senior engineers, and help improve the performance and reliability of generative AI systems. Interns may also be involved in data preprocessing, model evaluation, and keeping up with the latest research in artificial intelligence.

What skills and qualifications are needed to thrive as a generative AI engineer intern?

To thrive as a Generative AI Engineer Intern, you need a solid understanding of machine learning fundamentals, programming skills (especially in Python), and coursework or experience in artificial intelligence or computer science. Familiarity with deep learning frameworks like TensorFlow or PyTorch and version control systems such as Git is typically required, and relevant coursework or certifications in AI/ML are advantageous. Strong problem-solving skills, curiosity, and the ability to communicate complex ideas clearly help interns stand out. These skills and qualities are crucial for quickly learning advanced AI techniques, contributing to team projects, and driving innovation in a rapidly evolving field.

What types of projects can a generative AI engineer intern expect to work on during their internship?

As a Generative AI Engineer Intern, you can expect to work on projects involving the development, training, and evaluation of generative models such as GANs, VAEs, or transformer-based architectures. Typical tasks may include data preprocessing, model implementation, fine-tuning, and running experiments to improve model performance. Interns often collaborate closely with data scientists, software engineers, and research teams, gaining exposure to both research and application of AI in real-world products. This role provides hands-on experience with state-of-the-art tools and frameworks, offering a valuable foundation for a future career in AI engineering or research.

What is the difference between Generative Ai Engineer Intern vs Machine Learning Engineer Intern?

AspectGenerative Ai Engineer InternMachine Learning Engineer Intern
Required CredentialsBasic knowledge of AI, programming, and some coursework in machine learning or AIStrong foundation in machine learning, programming, and data analysis, often with coursework or certifications
Work EnvironmentTech companies, startups, research labs focusing on AI applicationsTech firms, research institutions, and companies applying machine learning models
Industry UsageDeveloping generative models like GPT, DALL·E, and similar AI toolsBuilding predictive models, data pipelines, and machine learning algorithms

While both roles involve AI and machine learning, a Generative Ai Engineer Intern focuses specifically on creating generative models like text, images, or audio, whereas a Machine Learning Engineer Intern works broadly on developing and deploying various machine learning algorithms across different applications.

What are the most commonly searched types of Generative Ai Engineer jobs in Wisconsin? The most popular types of Generative Ai Engineer jobs in Wisconsin are:
What cities in Wisconsin are hiring for Generative Ai Engineer Intern jobs? Cities in Wisconsin with the most Generative Ai Engineer Intern job openings:
Infographic showing various Generative Ai Engineer Intern job openings in Wisconsin as of August 2026, with employment types broken down into 17% Internship, 66% Full Time, and 17% Part Time. Highlights an 100% In-person job distribution.

$130 - $160/hr

Other

Posted 3 days ago

New


Job description

Position Summary

The Sr AI Engineer serves as a technical leader responsible for enterprise-scale AI architecture, solution governance, and advanced AI engineering practices. This role drives strategic AI adoption and mentors engineering teams across the organization. This role will partner closely with Information Technology, business stakeholders, operations, customer service, product development, and analytics teams to deliver scalable AI capabilities that improve efficiency, enhance customer experiences, and enable data-driven decision making.

The ideal candidate combines strong software engineering fundamentals with practical expertise in machine learning, generative AI, data engineering, automation, and cloud technologies. This individual must be comfortable operating in a fast-paced, transformation-oriented environment and capable of translating business problems into production-ready AI solutions.

Key Responsibilities
  • Design, build, deploy, and maintain enterprise AI and machine learning solutions.
  • Develop and operationalize generative AI applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), copilots, and intelligent automation.
  • Partner with business leaders to identify high-value AI use cases aligned to strategic priorities.
  • Build scalable AI pipelines, APIs, and integrations with enterprise platforms and business applications.
  • Collaborate with data engineering teams to ensure high-quality, governed, and accessible data for AI initiatives.
  • Develop AI-enabled analytics and predictive models supporting manufacturing, supply chain, customer service, sales, and operations.
  • Implement AI governance, model monitoring, security, and responsible AI practices.
  • Optimize model performance, scalability, reliability, and operational efficiency.
  • Evaluate emerging AI technologies and recommend enterprise adoption strategies.
  • Support AI experimentation, rapid prototyping, and innovation initiatives across the organization.
  • Create technical documentation, operational procedures, and knowledge transfer materials.
  • Mentor technical teams and promote AI engineering best practices.
Requirements

Required

  • Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
  • Advanced degree in Artificial Intelligence, Machine Learning, or Data Science preferred.
  • 8-10+ years overall technology experience
  • 5+ years focused in AI/ML engineering
  • Proven experience deploying enterprise-scale AI systems
  • Experience leading technical teams or major initiatives
  • Strong experience with AI architecture and distributed systems
  • Experience operationalizing generative AI at scale
  • Executive communication capability

Preferred

  • Experience with Microsoft Copilot, Azure OpenAI, or enterprise generative AI platforms.
  • Manufacturing, supply chain, consumer products, or retail industry experience.
  • Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.
  • Familiarity with data visualization and analytics platforms such as Power BI or Tableau.
  • Experience leading enterprise AI transformation initiatives.
  • AI governance frameworks
  • FinOps for AI workloads
  • Multi-cloud AI strategy
  • Experience building internal AI platforms or copilots
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