Detroit Defense is seeking a motivated AI Engineer to help design and implement intelligent systems that learn from data, make predictions, and simulate complex real-world processes. The ideal candidate has a strong data science foundation and is eager to apply that knowledge to practical AI applications. This role offers an opportunity to work closely with experienced engineers and researchers on projects spanning classification, prediction, and simulation models across a range of domains. Essential Duties & Responsibilities: Design and implement algorithms for classification, prediction, and simulation based on real-world datasets. Develop, train, and evaluate models using Python and modern machine learning frameworks. Collaborate with engineers and subject matter experts to transform data into actionable insights and AI-driven solutions. Contribute to data preparation, feature engineering, and performance analysis. Assist with model validation and documentation to ensure accuracy and explainability. Participate in collaborative design discussions, code reviews, and prototype development. Demonstrate a willingness to learn MLOps practices, including model deployment and monitoring workflows. Requirements: Bachelor’s degree in Data Science, Computer Science, Applied Mathematics, Statistics, or a related field. Solid understanding of machine learning fundamentals — including supervised and unsupervised learning, classification, regression, and performance metrics. Proficiency in Python and familiarity with common ML libraries (e.g., scikit-learn, NumPy, pandas, PyTorch, or TensorFlow). Experience with data analysis and visualization tools such as Jupyter, Matplotlib, or seaborn. Strong problem-solving and analytical skills, with the ability to interpret results and communicate findings clearly. Curiosity and initiative to explore new modeling approaches and technologies. Preferred Qualifications: Project or internship experience applying AI or data science to a real-world problem. Understanding of simulation modeling, time-series forecasting, or predictive maintenance concepts. Familiarity with data pipelines or cloud environments is a plus, but not required. Exposure to principles of Responsible AI, data ethics, or explainable AI
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