AI Engineer Intern

UNION HOME MORTGAGE CORP•Strongsville, OH

1d•Onsite

About The Position

Union Home Mortgage’s L.E.A.D Internship Program’s goal is to provide a fun, interesting, and real-world environment for our interns to Learn about the industry, be Educated by Sr. Leadership and their peers, Achieve their personal goals and Develop their skills and knowledge base. We pride ourselves in providing innovative programs for our interns in order for them to learn and grow they progress through their careers. Some of the programs we offer include: shadowing, mentoring, professional development, group projects and we even take our interns on corporate outings! Our internship program gives students a chance to meet new people, gain more experience, and learn from the best in the business! Our interns are treated like full-time Partners who work 40 hours a week during the 3-month summer program, are compensated, and based out of headquarters in Strongsville, Ohio. An AI Engineer Intern will support the research, development, and evaluation of artificial intelligence solutions designed to improve mortgage operations, including loan origination, servicing, workflow efficiency, and customer experiences. In this role, you will apply foundational knowledge of machine learning, neural networks, Transformer architectures, large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI systems to real-world business challenges. Working alongside experienced engineers and business stakeholders, you will research technical approaches, develop prototypes, test and evaluate AI systems, and help determine where AI can meaningfully improve processes and reduce manual effort.

Requirements

Basic to intermediate programming proficiency in Python and familiarity with software development concepts
Foundational understanding of machine learning, including supervised/unsupervised learning, training and validation, loss functions, optimization, overfitting, generalization, and model evaluation
Foundational understanding of neural networks and deep learning, including layers, weights, activation functions, forward propagation, backpropagation, and gradient-based learning
Ability to explain the fundamentals of Transformer architectures, including attention/self-attention, tokens, embeddings, context windows, and autoregressive generation
Understanding of tokenization and embeddings, including how text is represented for LLMs and how embeddings support semantic search and retrieval
Familiarity with large language models (LLMs), including pretraining, inference, prompting/context engineering, tool calling, model limitations, hallucinations, and evaluation
Familiarity with RAG, fine-tuning, and model adaptation, including the high-level differences between prompting, retrieval, and fine-tuning approaches
Interest in AI agents and agentic workflows, including tool use, state, memory, routing, and orchestration; exposure to LangGraph, LangChain, or similar frameworks is a plus
Exposure to AI/ML libraries or frameworks such as PyTorch, TensorFlow, Hugging Face, scikit-learn, NumPy, or pandas is preferred
Strong analytical, problem-solving, communication, and technical research skills with the ability to explain how and why an AI system works, not simply how to use it
Currently pursuing a bachelor's or graduate degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, Software Engineering, Mathematics, Statistics, or a related technical discipline
Foundational understanding of the mathematical and computational principles underlying modern AI and machine learning
Experience through coursework, research, independent study, personal projects, hackathons, open-source contributions, or previous internships is welcomed
Demonstrated ability to research technical concepts, experiment with different approaches, evaluate results, and clearly communicate findings

Nice To Haves

A GitHub profile, AI/ML project, research project, technical portfolio, or similar demonstration of technical work is preferred
Prior professional AI engineering or machine learning experience is not required
Relevant coursework in artificial intelligence, machine learning, deep learning, natural language processing, algorithms, statistics, probability, linear algebra, or calculus is preferred

Responsibilities

Explore and prototype AI/ML solutions that improve business processes and customer experiences
Identify opportunities to apply AI and automation to reduce manual effort
Assist in developing LLM, RAG, agentic, and traditional machine learning workflows
Evaluate AI outputs for accuracy, reliability, performance, and appropriate business use
Assist with safeguards, testing, documentation, and human-in-the-loop processes
Work with technical and business stakeholders to understand problems and develop appropriate solutions
Stay current with developments in AI, machine learning, LLMs, and related technologies and evaluate their potential application