AI Engineering Intern

Crowe•Chicago, IL

3d•$27 - $42

About The Position

The AI Engineering Intern (Intern) supports the design, development, testing, and deployment of artificial intelligence solutions across the organization. This hands-on role provides exposure to core AI engineering practices—including model development, data preparation, and prototype creation—while contributing to supervised project tasks. Working closely with senior engineers, the intern builds foundational skills in coding standards, version control, evaluation techniques, and cloud-native development workflows. The intern collaborates with cross-functional teams, applies academic concepts to enterprise-scale systems, and gains the experience needed to grow into an entry-level engineering role. This role emphasizes learning and skill development while delivering on assigned components, working under close supervision with frequent check-ins and code reviews, and building foundational technical skills and understanding of engineering practices. Successful interns demonstrate curiosity, initiative, and responsiveness to feedback.

Requirements

Pursuing a degree in Computer Science, Engineering, Data Science, or a related field
Working knowledge of foundational programming concepts and data structures, with exposure to Python
Familiarity with core AI/ML concepts gained through coursework or independent projects
Ability to communicate technical ideas clearly and collaborate effectively within a team
Exposure to cloud platforms such as AWS, Azure, or GCP, along with containerization concepts including Docker and Kubernetes
Introductory understanding of CI/CD practices and distributed computing fundamentals
Curiosity-driven approach to learning, with responsiveness to feedback and coaching
Some grounding in generative AI frameworks such as PyTorch, TensorFlow, or Hugging Face
Beginning awareness of LLM concepts, including RAG workflows, vector databases, and fine-tuning techniques
Basic ML/AI literacy (training vs inference, knowledge cutoffs, LLM fundamentals)
Prompt engineering and instruction hierarchies
Context window and context management
Model selection and capabilities
Fine-tuning vs prompting vs RAG
Hallucination and grounding strategies
Guardrails and output validation
Evals and testing approaches
Security and PII awareness
Responsible AI and governance
LLM APIs and integration standards
RAG and vector search
Vector databases and embeddings
Agentic workflows and tool use
Cost and performance awareness
Common enterprise use cases
Intellectual curiosity – asking thoughtful questions and seeking deeper understanding
Attention to detail – noticing subtle issues and inconsistencies
Analytical thinking – breaking down complex problems and thinking critically
Tenacity – following issues through to resolution, even when challenging
Strong communication – conveying ideas clearly to technical and non-technical audiences
Uphold Crowe’s values of Care, Trust, Courage, and Stewardship

Nice To Haves

Academic or project-based experience applying AI/ML concepts
Awareness of low-code automation platforms such as Microsoft Power Platform (Power Apps, Power Automate)
Familiarity with developer-focused AI tools such as Replit Agent or similar agentic coding platforms

Responsibilities

Support the development of AI models and prototypes through data preparation, scripting, and experimentation
Assist in implementing components of AI pipelines, including preprocessing, model training, and testing workflows
Participate in code reviews and supervised engineering tasks to learn best practices
Contribute to documentation of prototypes, experiments, and engineering processes
Execute test cases and validation procedures to verify model performance and reliability
Analyze datasets to extract insights and generate features under guidance
Learn cloud-based tools, containerization, and orchestration concepts relevant to AI deployment
Collaborate with team members to solve technical problems and complete scoped tasks
Follow established coding, security, and compliance guidelines
Explore emerging AI technologies and frameworks to build foundational understanding