Machine Learning Engineer
Job Description
The Machine Learning Engineer is responsible for designing, deploying, and maintaining advanced artificial intelligence models within secure defense computing networks. The role ensures the successful execution of algorithmic architectures, neural network training pipeline development, and prototype transition to active mission workflows while upholding strict federal data security and classification protocols.
Required Qualifications * Master’s or Bachelor’s degree in Computer Science, Data Science, AI Engineering, or a highly quantitative field.
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3–5 years of proven experience building and operationalizing production-level ML architectures.
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Active and verifiable TS/SCI security clearance is mandatory.
Preferred Skills * Masterful command of Python, C++, Docker, Kubernetes, and secure MLOps platforms.
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Deep structural familiarity with specialized defense data formats and sensor frameworks.
Experience Required 3–5 years of relevant experience deploying production machine learning models within enterprise or defense/intelligence infrastructure.
Responsibilities Duties:
Key Responsibilities * Coordinate and execute full-stack machine learning lifecycles, specializing in deep learning, computer vision, or NLP solutions.
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Prepare optimized cloud and on-premise training environments for massive, classified datasets.
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Develop high-performance algorithmic solutions using frameworks such as PyTorch or TensorFlow.
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Coordinate with data engineering, embedded systems teams, and intelligence analysts for microservice model delivery.
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Monitor model performance during integration to promptly address data drifts and execution latencies.
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Ensure proper handling and transmission of classified assets in strict accordance with defense clearance provisions.