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AI Engineer (40% Telework) with Security Clearance
MasterPeace Solutions, Ltd.Fort Meade, MD🇺🇸United StatesPosted 13 Jul 2026
Quick Overview
Work Type
Remote
Level
Mid Senior
Job Description
Required technical skills and responsibilities include:
• Design and Development: Lead the design and development of AI-driven solutions from conception to deployment, ensuring seamless integration with the existing software architecture. This includes prototyping new models, writing production-quality code, and maintaining existing AI systems.
• Collaboration and Communication: Serve as a key technical liaison, collaborating with cross-functional teams including system engineers, software developers, and domain experts. Effectively present and articulate recommended AI approaches, discussing the tradeoffs and implications of different implementations with both technical and non-technical stakeholders.
• Data Analysis and Modeling: Conduct Exploratory Data Analysis (EDA) on diverse datasets (both structured and unstructured) to inform the data model, identify data quality issues, and determine optimal input formats for AI models.
• Model Building and Evaluation: Develop, train, and evaluate a variety of machine learning models, ensuring they meet performance and reliability requirements. Implement robust testing and validation strategies to ensure models are accurate and unbiased.
• Cloud Platforms: Have experience with the Amazon Web Services (AWS) cloud computing platform and machine learning operations (MLOps) tools for deploying and managing machine learning models at scale.
• Design and Development: Lead the design and development of AI-driven solutions from conception to deployment, ensuring seamless integration with the existing software architecture. This includes prototyping new models, writing production-quality code, and maintaining existing AI systems.
• Collaboration and Communication: Serve as a key technical liaison, collaborating with cross-functional teams including system engineers, software developers, and domain experts. Effectively present and articulate recommended AI approaches, discussing the tradeoffs and implications of different implementations with both technical and non-technical stakeholders.
• Data Analysis and Modeling: Conduct Exploratory Data Analysis (EDA) on diverse datasets (both structured and unstructured) to inform the data model, identify data quality issues, and determine optimal input formats for AI models.
• Model Building and Evaluation: Develop, train, and evaluate a variety of machine learning models, ensuring they meet performance and reliability requirements. Implement robust testing and validation strategies to ensure models are accurate and unbiased.
• Cloud Platforms: Have experience with the Amazon Web Services (AWS) cloud computing platform and machine learning operations (MLOps) tools for deploying and managing machine learning models at scale.
Skills
AWS
MLOps
Machine Learning
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