About the role

AI Engineer translating cutting-edge AI concepts into mission-scale solutions for the warfighting community. Collaborate with researchers and government sponsors to develop secure, reliable AI capabilities.

Responsibilities

translate cutting-edge AI concepts into robust, mission-scale solutions for the warfighting community
work with large-scale foundation models such as GPT and LLaMA, designing and deploying agentic workflows
apply and advance traditional ML research and engineering across domains such as natural language processing, computer vision, time series forecasting, and other predictive analytics
collaborate closely with senior researchers, software engineers, and government sponsors to define problem statements and iterate on experimental designs
deliver secure, reliable AI capabilities that meet stringent mission requirements
design, develop, and fine-tune a variety of AI models
design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration
build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning

Bachelor’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field
at least four years of relevant experience (or an M.S. with two years)
Ability to obtain and maintain an active Department of War (DoW) security clearance
Proficiency in Python and at least one compiled language (C/C++ or Java)
Experience with REST/GraphQL APIs and containerization
Strong grasp of ML theory (supervised, unsupervised, reinforcement learning) and evaluation metrics
Hands-on experience fine-tuning LLMs and using frameworks such as Hugging Face Transformers, LangChain, or comparable agent tools
Familiarity with building RAG pipelines (vector stores, dense/sparse retrievers)
Experience applying PEFT/LoRA methods (e.g., LoRA, adapters) to large models
Understanding of Model Context protocols for managing model state across multi-turn interactions
Experience building evaluation frameworks, benchmarks, or data quality pipelines
Experience with TensorFlow, PyTorch, or JAX; knowledge of data-pipeline tools (Airflow, Prefect, Ray) is a plus
Awareness of DevSecOps practices (CI/CD, GitOps, container security scanning, model-registry concepts) is desirable