Posted Feb 11, 2026
About Agile Defense
At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next. Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests. Requisition #: 1418
**Job Title:**ML Engineer
Location: Omaha, NE
Clearance Level: Active DoD Top Secret
Overview:
At Agile Defense we know that action defines the outcome and new challenges require new solutions. That’s why we always look to the future and embrace change with an unmovable spirit and the courage to build for what comes next. Our vision is to bring adaptive innovation to support our nation's most important missions through the seamless integration of advanced technologies, elite minds, and unparalleled agility—leveraging a foundation of speed, flexibility, and ingenuity to strengthen and protect our nation’s vital interests. We are currently looking for an ML Engineer to support our contract with the DRAID CDAO ADA IR Program. As an ML Engineer at Agile Defense, you will be joining a team of professionals that secure, scalable data architectures and AI/ML pipelines. This role will support data science and engineering activities, partnering with product teams, data engineers, mission stakeholders, and technologists to unlock the value of structured and unstructured data in support of national defense priorities. You will implement data engineering activities and develop and deploy pipelines and platforms that organize and make complicated data meaningful.
Build Scalable Data & ML Infrastructure
· Design and implement medallion architecture (Bronze/Silver/Gold) using Databricks for reliable data processing and ML model training
· Develop automated data pipelines that process structured and unstructured data from multiple sources into analytics-ready formats
· Create robust ETL/ELT workflows using Apache Spark and modern data engineering practices for both batch and streaming data
· Build and maintain data quality monitoring and validation systems across the entire data and ML lifecycle
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