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# AI Infrastructure Engineer

- **Company:** 10x National Security, LLC
- **Location:** Bethesda, MD, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Batch Processing, Network Analysis, Encodings, Information Engineering, Distributed Systems, Graph Database, Intelligence Analysis, Python (Programming Language), Machine Learning, Rapid Prototyping Process, Tensorflow, Zero Trust Network Access, Unstructured Data, AI Infrastructure, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Apache Spark, Deep Learning, Gitlab, Pandas, Containerization, Scikit Learn, Kubernetes, Information Technology, Data Analytics, Enterprise Integration, Integration Frameworks, Machine Learning Operations, Restful APIs, Data Pipelines, Devsecops, Docker, Microservices
- **Published:** June 28, 2026
- **Apply:** https://www.clearancejobs.com/jobs/8843206/ai-infrastructure-engineer

## About the Role

Active TS/SCI clearance (or ability to obtain)

 - Bachelor's or Master's in Computer Science, AI, Data Science, Engineering, or related field
 - 3-10+ years of experience in AI/ML engineering or applied data science

Technical Expertise
Strong proficiency in:

 - Python (PyTorch, TensorFlow, Scikit-learn)
 - Data frameworks (Pandas, Spark, Ray)
Experience with:

 - Graph analytics and network analysis
 - Anomaly detection and behavioral modeling
 - Entity resolution and probabilistic matching
Familiarity with:

 - Kubernetes, Docker, microservices architectures
 - REST APIs and distributed systems, Experience supporting DIA, IC, or DoD AI/ML programs
Hands-on experience with:

 - NVIDIA Morpheus or GPU-accelerated AI pipelines
 - Vector databases and embedding-based search
 - Knowledge graphs and semantic reasoning systems

Experience operating in:

 - DDIL (Disconnected, Denied, Intermittent, Low-bandwidth) environments
 - Edge AI deployments

## Description

The AI Engineer will design, develop, and deploy scalable machine learning and AI-driven analytics capabilities

 - Multi-source data fusion
 - Entity resolution and behavioral modeling
 - Predictive and prescriptive intelligence analytics
 - Autonomous detection and alerting pipelines

You will operate across the full lifecycle from data ingestion to model deployment to operational feedback loops.

Core Responsibilities
AI/ML Engineering & Model Development

 - Design and implement machine learning, deep learning, and statistical models for intelligence use cases
 - Build entity resolution, graph analytics, and behavioral anomaly detection models
 - Develop adaptive models that evolve with adversary tactics, techniques, and procedures (TTPs)
 - Leverage transformer architectures, LLM fine-tuning, and retrieval-augmented generation (RAG) where mission-appropriate
Data Engineering & Pipeline Integration

 - Integrate models into high-throughput data pipelines supporting structured, semi-structured, and unstructured data
 - Work with streaming frameworks and batch processing systems to enable real-time inference at scale
 - Implement feature engineering pipelines aligned with mission-relevant signals and intelligence context
Operational Deployment (MLOps / DevSecOps)

 - Deploy models into Kubernetes-based, containerized environments across cloud and edge
 - Build CI/CD pipelines in GitLab for automated training, testing, validation, and deployment
 - Implement model monitoring, drift detection, and continuous retraining pipelines
 - Ensure compliance with Zero Trust Architecture (ZTA) and IC security requirements
Explainability & Analyst Integration

 - Deliver traceable, explainable AI outputs suitable for analyst validation and operational decision-making
 - Build interfaces and APIs that enable human-in-the-loop workflows and override capabilities
 - Ensure all models maintain provenance, auditability, and reproducibility
Collaboration & Mission Alignment

 - Work directly with intelligence analysts, operators, and mission stakeholders
 - Translate mission problems into technical AI solutions with measurable outcomes
 - Contribute to a culture of rapid prototyping, iteration, and deployment


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