AI Infrastructure Engineer

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

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