ML) Engineer

University of Southern Mississippi•Gulfport, MS

59d•Onsite

About The Position

This is an early to mid-career role that will require the incumbent to design, build and maintain machine learning (ML) models focusing on making AI models practical and scalable for real-world applications, often bridging the gap between data science and software engineering. The position is located at the USM Roger F. Wicker Center for Ocean Enterprise.

Requirements

Bachelor's degree from an accredited college or university in Computer Science, Data Science, Engineering or a related field and 6 years of experience or a Master's Degree from an accredited college or university in Computer Science, Data Science, Engineering or a related field and 4 years of experience.
Demonstrated experience working in the full lifecycle of an ML Model.
U.S. Citizenship with the ability to obtain a Government clearance.
A valid driver's license and eligibility to receive a Trusted Worker Identification Card (TWIC)
Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, and XGBoost.
Experience with data manipulation tools like Pandas, NumPy, and SQL.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud (especially services like SageMaker, Vertex AI, etc.).
Experience with model deployment tools and frameworks (e.g., Docker, FastAPI, Flask, MLflow).
Strong understanding of supervised and unsupervised learning, neural networks, NLP, and computer vision.
Experience with version control systems (e.g., Git) and CI/CD pipelines.
Excellent problem-solving skills and ability to work independently and in teams.

Nice To Haves

Experience with MLOps practices and tools.
Familiarity with big data technologies (e.g., Spark, Hadoop).
Knowledge of data visualization tools (e.g., Matplotlib, Seaborn, Plotly).
Contributions to open-source projects or published research in AI/ML
Proficiency in programming languages like C, Java, C++ and Python

Responsibilities

Design, develop, and optimize machine learning models for diverse business and research applications.
Collaborate with data scientists, software engineers, and product teams to integrate ML models into production systems and ensure scalability.
Lead data preparation efforts, including preprocessing, feature engineering, and exploratory data analysis to support model development.
Evaluate and refine models using appropriate metrics, improving accuracy, efficiency, and robustness against real-world data challenges.
Maintain and enhance ML pipelines and infrastructure, ensuring reliability, reproducibility, and continuous improvement.
Stay current with AI/ML research and apply emerging methods to solve complex practical problems.
Translate research into practice by collaborating with principal investigators and research teams, and assisting in preparing technical reports, visualizations, and academic publications.
Document models, processes, and codebases to ensure transparency, reproducibility, and effective knowledge sharing across teams.

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