AI Engineer I

Universal Corporation•Richmond, VA

9h•$66,000 - $88,300•Hybrid

About The Position

As an AI Engineer I at Universal, you will design and develop scalable AI solutions that support business innovation and operational efficiency. You will contribute to the full lifecycle of AI systems, from prototyping and model training to performance testing and deployment. The position is intended for early-career professionals with strong technical foundations and an interest in machine learning and big data technologies. The role is based at Universal Corporation’s Richmond Headquarters in the Information Systems department and follows a hybrid arrangement of three days in the office and two days remote. It is full-time and salaried exempt, and reports to the AI Project Manager. Candidates must be legally authorized to work in the United States; Universal Corporation cannot provide visa sponsorship or transfer sponsorship now or in the future.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
Proficiency in programming languages such as .NET, Python, or R.
Internship or project experience in AI/ML demonstrating hands-on exposure.
Basic understanding of statistics and data analysis.
Experience with SQL or NoSQL databases.
Ability to write clean, maintainable code and follow coding standards.
Strong analytical and problem-solving skills.
Effective communication and collaboration abilities.
Must be legally authorized to work in the United States; visa sponsorship or transfer sponsorship is not available.

Nice To Haves

Exposure to MLOps tools and practices.
Experience with RESTful APIs or microservices.
Familiarity with containerization, such as Docker.
Experience working in Agile/Scrum environments.
Experience with data visualization and version control, such as Power BI and Git.

Responsibilities

Design and develop scalable solutions using AI tools and machine learning models.
Ensure the scalability, reliability, and performance of AI systems across diverse use cases.
Develop and execute performance tests to assess system efficiency with large datasets, complex algorithms, and processing speeds.
Research and test machine learning algorithms and predictive models.
Use big data computation and storage tools to create prototypes and datasets.
Train, evaluate, and tune models to optimize performance.
Integrate, test, and monitor AI solutions in collaboration with cross-functional teams.
Document technical workflows and contribute to team knowledge sharing.
Stay current with emerging AI tools and technologies to support continuous improvement.