Posted 13 Mar 2026, 7:01 pm
AI Engineer (40% Telework) with Security Clearance
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.
Originally posted on Himalayas
