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AI Engineer with Security Clearance

Delviom LLCWashington, DC🇺🇸United StatesPosted Oct 9, 2026

Quick Overview

Seniority
Mid Senior
Employment type
Employee
Work mode
Hybrid
Location
Washington, DC, United States
Posted
Yesterday
MLOpsMachine LearningDeep LearningPythonTensorFlow

Job Description

Mandatory Qualifications and Experience
Bachelor’s degree in Computer Science, Information Technology, Mathematics, Engineering, or a related STEM discipline. Minimum of two years of relevant professional experience in AI, Machine Learning, or related technical domains. Hands-on experience developing AI/ML applications using Python. Practical knowledge of Machine Learning and Deep Learning algorithms, methodologies, and applications. Strong understanding of mathematical and statistical concepts used in AI/ML model development.

Experience in data collection, preprocessing, transformation, analysis, and quality management. Experience building AI/ML solutions for automation, predictive analytics, Natural Language Processing, or pattern recognition. Familiarity with the end-to-end AI/ML lifecycle, including model development, testing, validation, deployment, monitoring, and maintenance. Understanding of responsible AI principles, including ethical AI, fairness, bias mitigation, data security, and trustworthy AI practices.

Ability to develop and support AI/ML solutions in accordance with established technical, security, and operational standards.

Technologies and Tools
Programming Language: Python
Artificial Intelligence: AI/ML application development and AI services
Machine Learning: Supervised and unsupervised learning techniques
Deep Learning: Neural networks and deep learning methodologies
Natural Language Processing: Text processing and language-based AI applications
Pattern Recognition: Data-driven pattern identification and classification
Predictive Analytics: Forecasting, prediction, and statistical modeling
Data Handling: Data preprocessing, transformation, validation, and quality management
Frameworks: TensorFlow
Model Operations: Model deployment, monitoring, retraining, and lifecycle management (MLOps)

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