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

Required Qualifications
- 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

Preferred Qualifications (Differentiators)
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