ML Engineer

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. Responsibilities/Duties 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 Drive ML Platform Excellence Implement MLOps best practices including automated model training, validation, deployment, and monitoring using MLflow and Databricks workflows Design scalable ML inference systems that handle high-volume, low-latency predictions in production environments Create comprehensive monitoring and alerting systems for model performance, data drift, and system health Build self-service ML capabilities that enable data scientists to deploy and monitor their own models efficiently Enable Advanced Analytics & Business Intelligence Design and maintain data models that support both machine learning workloads and business intelligence requirements Create integration points between ML systems and business intelligence platforms (Tableau, PowerBI, Qlik Sense) Implement data governance standards and metadata management systems that ensure data quality and compliance Collaborate with analysts and data scientists to optimize data architecture for both predictive modeling and reporting needs Ensure Data Quality & Governance Implement comprehensive data governance frameworks including data lineage tracking, quality monitoring, and compliance controls Design and maintain data catalogs and metadata management systems that enable efficient data discovery across the organization Establish data quality standards and automated testing frameworks for both analytical and ML workloads Work with stakeholders to define data definitions, business logic, and governance policies Integrate with Enterprise Systems Build integrations with MAVEN Smart Systems (Palantir Foundry) environments to support operational and predictive analytics Connect Databricks-based systems with enterprise data warehouses, streaming platforms, and business applications Implement security and compliance controls that meet enterprise requirements while enabling self-service capabilities Collaborate with platform engineers to integrate ML systems with broader application architecture and infrastructure Required Skills – What You’ll Bring 5+ years of technical experience, including 3+ years building production data pipelines and ML infrastructure using distributed computing platforms like Databricks. Strong data engineering skills in Python, PySpark, and Spark SQL with experience implementing medallion architecture and modern data platform patterns Production ML systems experience including model deployment, monitoring, and MLOps practices in cloud environments Data architecture expertise with experience designing scalable data processing systems and implementing data governance frameworks Experience integrating with platforms such as Qlik, Tableau, PowerBI, MAVEN Smart System (Palantir), or similar. Preferred Skills - What Would Set You Apart Deep expertise in distributed computing, performance optimization, and large-scale data processing using Databricks and Apache Spark Advanced MLOps knowledge including automated retraining, model versioning, model testing frameworks, and production ML monitoring Experience conducting regression analysis, and building predictive models for business applications with measurable impact Advanced statistical knowledge including experimental design, hypothesis testing, causal inference, and statistical modeling techniques Experience designing and building enterprise-level dashboards, reports, and self-service analytics platforms Analytics platform knowledge including experience with Advana / MAVEN Smart Systems (Palantir Foundry) or similar enterprise analytics environments Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities #J-18808-Ljbffr