AI Engineer I

Universal Corporation•Richmond, VA

23h•$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. This role is ideal for early-career professionals with strong technical foundations and a passion for machine learning and big data technologies.

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 (demonstrates 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.
Candidates must be legally authorized to work in the United States. Universal Corporation is unable to provide visa sponsorship or transfer sponsorship for this position now or in the future.

Nice To Haves

Exposure to MLOps tools and practices.
Experience with RESTful APIs or microservices
Familiarity with containerization (Docker)
Experience working in Agile/Scrum environments
Experience with data visualization and version control (e.g., Power-BI, 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 evaluate system efficiency in handling large datasets, complex algorithms, and processing speeds.
Perform research and testing to develop machine learning algorithms and predictive models.
Utilize big data computation and storage tools to create prototypes and datasets.
Conduct model training, evaluation, and tuning to optimize performance.
Integrate, test, and monitor AI solutions in collaboration with cross-functional teams.
Document technical workflows and contribute to knowledge sharing across the team.
Stay current with emerging AI tools and technologies to support continuous improvement.