AI/ML Engineer

Responsibilities

• Design and implement AI capabilities supporting intelligent data characterization, classification, prioritization, and decision support.
• Evaluate, optimize, and deploy open-weight foundation models appropriate for resource-constrained edge environments.
• Develop efficient inference pipelines supporting heterogeneous compute environments ranging from embedded processors to workstation-class systems.
• Implement Retrieval-Augmented Generation (RAG), semantic search, and knowledge retrieval capabilities where appropriate.
• Design AI orchestration workflows supporting distributed inference across multiple edge devices.
• Develop evaluation methodologies for AI accuracy, latency, resource utilization, and operational performance.
• Implement model monitoring, observability, testing, and automated evaluation frameworks.
• Collaborate with software engineers to integrate AI models into production software platforms.
• Optimize models using quantization, pruning, distillation deployment technologies.
• Support experimentation involving multimodal data sources, sensor-derived features, and structured mission data.
• Develop AI governance practices including model evaluation, explainability, responsible AI, and secure deployment.
• Document model development, evaluation results, and technical recommendations.
• Support customer demonstrations and prototype evaluations.

Qualifications

• Bachelor degree in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, Applied Mathematics, or related discipline. An advanced degree is preferred.
• 5-8+ years of professional experience developing production AI or machine learning applications.
• Strong Python programming experience.
• Experience with PyTorch.
• Experience deploying LLMs in production environments.
• Experience with LangGraph, LangChain, CrewAI, Semantic Kernel, or similar orchestration frameworks.
• Experience implementing Retrieval-Augmented Generation (RAG).
• Experience with vector databases and semantic search.
• Experience deploying AI models on edge or resource-constrained devices.
• Experience with model optimization techniques including quantization, model compression, or inference acceleration.
• Experience designing evaluation frameworks for AI systems.
• Experience with Docker and cloud-native AI deployment.
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience applying AI to sensor analytics, time-series data, or signal processing.
• Experience with software-defined radio data, RF analytics, or geospatial data analytics.
• Experience developing multimodal AI applications.
• Experience deploying AI across distributed edge computing environments..
• Experience supporting DoD, Intelligence Community, or Federal customers.
• Experience working in bandwidth-constrained or disconnected operational environments.
• Experience supporting National Security or Federal Civilian customers.