Interested in this AI/ML Engineer role at Hollister Incorporated?
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About This Role
We Make Life More Rewarding and Dignified
Location: Libertyville
Department: Information Technology
Position Summary
Join the Data & AI team as an Applied AI Intern and contribute to real-world enterprise AI solutions. You will help design, build, test, and improve generative AI agents, knowledge assistants, retrieval solutions, and intelligent workflows that support business, R&D, Quality, and enterprise operations. The role combines hands-on solution development with applied research, evaluation, documentation, and responsible AI practices.
This internship is designed for talented undergraduate and master’s candidates who are eager to apply AI, software engineering, and analytical skills in a collaborative corporate environment. The intern will work closely with AI architects, engineers, IT specialists, and business stakeholders while learning how AI solutions move from business need and prototype to governed enterprise deployment.
Hollister Incorporated operates in a hybrid environment, Interns are required to come into the office in Libertyville Illinois a minimum of 4 days per week. Hollister does not provide relocation nor housing assistancefor this opportunity.
Responsibilities
- Contribute to AI Agent Development: Assist with the design, configuration, development, and testing of enterprise AI agents and copilots using Microsoft Copilot Studio, Azure AI technologies, Python, APIs, and related tools.
- Build Knowledge and Retrieval Solutions: Support retrieval-augmented generation (RAG), Knowledge Graph, enterprise search, document intelligence, semantic and vector search, metadata filtering, citations, and grounded response patterns across structured and unstructured information.
- Evaluate and Improve AI Quality: Create and execute evaluation scenarios; assess accuracy, relevance, grounding, citations, retrieval quality, response consistency, and usability; document defects and recommend improvements.
- Analyze Enterprise Content: Review SharePoint sites, documents, PDFs, spreadsheets, images, and other knowledge sources for AI readiness. Help identify OCR, content-quality, metadata, access, and indexing requirements.
- Prototype AI Solutions: Conduct focused research on emerging AI models, frameworks, APIs, orchestration approaches, and platform capabilities. Build small proof-of-concepts to assess technical feasibility and business value.
- Support API and System Integration: Help connect AI agents with enterprise services, custom connectors, APIs, Azure Functions, data platforms, and business applications under the guidance of senior team members.
- Apply Responsible AI and Security Practices: Follow organizational requirements for data protection, privacy, security, access control, human review, source grounding, and responsible use of AI. Avoid exposing credentials, sensitive information, or unsupported claims.
- Use Collaborative Development Practices: Work with GitHub, source control, branching, pull requests, documentation, and deployment pipelines to support collaboration, traceability, and recoverability of AI solutions.
- Collaborate Across Functions: Participate in requirements discussions, demonstrations, working sessions, and testing with IT, R&D, Quality, Data, Automation, and business teams.
- Document and Present Results: Maintain clear technical notes, solution documentation, test evidence, project updates, and implementation guidance. Present project outcomes and recommendations at the end of the internship.
Potential Project Exposure
- Enterprise knowledge agents and business-facing copilots, including Competitive Intelligence, Market Access, Finance, and R&D knowledge assistants.
- Quality Document Intelligence initiatives involving document search, comparison, metadata, product drawings, DHF/DMR analysis, traceability, and compliance-oriented workflows.
- Reusable AI-agent evaluation frameworks for regression testing, quality measurement, and release validation.
- Enterprise AI platform capabilities such as retrieval services, agent orchestration, knowledge graphs, context engineering, observability, governance, and reusable development standards.
- Digital Twin and simulation-related AI experiences, including data preparation, result analysis, workflow automation, and user interaction improvements.
- AI knowledge-source preparation, OCR assessment, SharePoint integration, GitHub collaboration, and CI/CD practices for AI solutions.
Requirements
- Education: Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, Information Systems, Engineering, or a related field. Candidate should remain enrolled in an academic program after the internship.
- AI and Software Fundamentals: Foundational understanding of artificial intelligence, machine learning, generative AI, large language models, software development, data structures, databases, or APIs through coursework or projects.
- Programming Skills: Experience with at least one programming language used for data or application development, preferably C# or Python. Ability to write understandable, maintainable code and troubleshoot technical issues.
- Communication Skills: Strong written and verbal communication skills, including the ability to explain technical concepts clearly to both technical and non-technical audiences.
- Collaboration and Learning: Curiosity, initiative, attention to detail, willingness to learn, and ability to work effectively with cross-functional teams in a fast-moving environment.
Preferred Skills (Bonus)
- Hands-on experience with generative AI, prompt engineering, AI agents, chatbots, RAG, embeddings, semantic search, or vector databases.
- Exposure Microsoft Copilot Studio, Microsoft Azure, Azure AI Search, Azure AI Foundry, Microsoft Fabric, Power Platform, or SharePoint.
- Experience using REST APIs, JSON, Git, GitHub, CI/CD pipelines, or cloud development environments.
- Familiarity with Python libraries for data analysis, machine learning, or AI application development.
- Experience designing test cases or evaluating AI/ML systems for accuracy, reliability, or responsible use.
- Awareness of AI ethics, privacy, security, governance, and the importance of human oversight in enterprise or regulated environments.
- Previous internship, research, hackathon, or substantial academic project involving AI, data, automation, or software development.
What You Will Learn
- How enterprise generative AI solutions are identified, designed, built, tested, governed, deployed, and improved.
- How agents use trusted enterprise data, retrieval, APIs, citations, and controls to produce grounded responses.
- How to evaluate AI quality systematically and use evidence to improve prompts, knowledge sources, tools, and workflows.
- How multidisciplinary teams collaborate to deliver AI solutions in a secure, responsible, and scalable manner.
- How modern engineering practices, documentation, source control, and release processes support production AI delivery.
Location
Libertyville, Illinois, USA – This internship is based at the Libertyville office and requires regular in-person collaboration with the team during Summer 2027. Specific hybrid-work expectations will be confirmed during the recruiting process. Relocation assistance, housing, and transportation are not provided for this opportunity.
The anticipated hourly rate for this position is $22.00 - $26.00 per hour based on applicant's education and skills.
About Hollister Incorporated
Hollister Incorporated is an independent, employee-owned company that develops, manufactures and markets healthcare products worldwide. The company spearheads the advancement of innovative products for ostomy care, continence care and critical care, and also creates educational support materials for patients and healthcare professionals. Headquartered in Libertyville, Illinois, Hollister has manufacturing and distribution centers on three continents and sells in nearly 80 countries. Hollister is a wholly owned subsidiary of The Firm of John Dickinson Schneider, Inc., and is guided both by its Mission to make life more rewarding and dignified for people who use our products and services, as well as its Vision to grow and prosper as an independent, employee-owned company, and in the process, to become better human beings.
EOE Statement
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
Job Req ID: 36703
Salary Context
This $45K-$54K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Hollister Incorporated, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($49K) sits 76% below the category median. Disclosed range: $45K to $54K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Hollister Incorporated AI Hiring
Hollister Incorporated has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Libertyville, IL, US. Compensation range: $54K - $54K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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