Manufacturing AI Engineer Co-op

Our Team

The Operations Engineering organization is made up of Process, Automation, Mechanical, Sustaining, and OT Engineers supporting manufacturing and product finishing operations across multiple campus buildings. The teams also include several co‑ops each year. This role works closely with the engineering leadership team and engineers across those disciplines and regularly collaborates with internal partners such as Quality, IT, Facilities, and Manufacturing.

Your Role

As an AI Engineering Co‑op, you will help build the knowledge‑layer infrastructure and reusable workflows that support Operations Engineering’s AI transformation. You will learn how engineers do their work, identify where better tooling and easier access to information would save meaningful time, and build, document, and support solutions the team can adopt and maintain after your term ends. This is an eight‑month, full‑time co‑op with two possible start dates in 2027 (January through August or May through December). Candidates must be able to commit to the full eight‑month term for their selected timeline.

Your Experience

We are looking for a naturally curious self‑starter with excellent communication and documentation skills and a demonstrated ability to work in cross‑functional teams. Comfort talking with subject‑matter experts, asking good questions, and learning an unfamiliar technical domain quickly is essential. Experience or interest in Python, SQL, prompt engineering, and retrieval‑augmented generation is important; experience in a regulated industry is a plus.

Core Duties

• Meet with engineers and subject‑matter experts to learn how they work, where their time goes, and what makes information hard to find, then translate those problems into practical solutions.
• Identify and prioritize use cases where better tooling or easier access to knowledge would save engineers meaningful time.
• Build and organize knowledge‑layer content, including context files, structured documentation, and retrieval patterns that help AI tools surface accurate answers from Operations Engineering knowledge.
• Prototype and test reusable workflows, then refine them into reliable solutions for everyday use.
• Document solutions clearly so engineers who did not build them can use, support, and modify them after the co‑op ends.
• Support adoption across the team through demonstrations, walkthroughs, quick reference guides, and hands‑on help.
• Capture recommendations on which solutions worked well and could be extended to other Manufacturing teams.
• Learn the Operations Engineering domain—including process control systems, production workflows, root‑cause analysis methodology, and documentation requirements—to build solutions that fit actual work practices.
• Assist with GMP documentation and tasks associated with engineering projects and knowledge‑management initiatives.
• Demonstrate inclusion through words and actions, maintain a safe workspace, and embody Promega’s 6 Emotional & Social Intelligence (ESI) core principles.
• Understand and comply with ethical, legal, and regulatory requirements applicable to the business.
• Perform other duties and responsibilities as assigned.

Expected Outcomes

• A small set of documented AI workflows in active use by the Operations Engineering team by the end of the co‑op.
• Improved knowledge accessibility, enabling engineers to find technical answers faster and with less effort.
• Reduced manual documentation effort on at least one recurring engineering task.
• Training materials and reference guides that help engineers apply new capabilities to their own work.
• Written recommendations on which solutions are worth extending to other Manufacturing teams.

Key Qualifications

• Currently enrolled in a Bachelor’s degree program in Systems Engineering, Industrial Engineering, Computer Science, Data Science, Machine Learning, or another engineering field with an AI specialty, and able to commit to an eight‑month full‑time co‑op.
• Natural curiosity about how engineers work and how AI tools can be applied, with the ability to translate descriptions into solution designs.
• Comfortable communicating with subject‑matter experts, asking questions, and quickly learning unfamiliar technical domains.
• Experience or interest in Python (scripting and APIs), SQL, prompt engineering, retrieval‑augmented generation (RAG), and large language models.
• Willingness to experiment with emerging AI tools and iterate when initial attempts do not work.
• Strong organization, communication, and documentation skills, including the ability to explain technical work to non‑specialists.
• Self‑starter who can manage multiple priorities and seek direction when needed.
• Proficiency with Microsoft 365 applications (Word, Excel, PowerPoint, Teams).
• Awareness of or interest in learning regulatory and compliance concepts in a manufacturing environment.

Preferred Qualifications

• Coursework, personal projects, or prior internship experience with generative AI platforms (e.g., Azure OpenAI, OpenAI API, Claude).
• Familiarity with Git and collaborative development practices.
• Experience building or documenting software solutions and technical workflows.
• Experience teaching, tutoring, or helping others adopt a new tool or process.
• Exposure to Databricks, Power BI, or similar analytics tools.
• Coursework or exposure to manufacturing concepts, process control systems, or production environments.
• Experience with knowledge‑management systems, SharePoint, or similar platforms.

Physical Demands

• Ability to remain stationary for several hours at a time.
• Ability to use a computer workstation and Microsoft 365 applications.
• Ability to work in an office environment.
• Ability to traverse between campus locations as needed for meetings and training.
• Occasional ability to observe equipment and operations in a production or lab environment.