17 hours ago
Washington, DC, USASenior
Responsibilities
- Lead systems integration and command-and-control development for AI/ML products in operational and exercise environments.
- Design, implement, and maintain scalable MLOps, data ingestion, preprocessing, feature engineering, model training, testing, deployment, monitoring, drift detection, and retraining pipelines.
- Architect enterprise data and ML platforms capable of processing massive-scale video, image, structured, and unstructured text data.
- Build real-time data integrations, APIs, microservices, data mesh solutions, and interoperable data-sharing capabilities across DoD systems.
- Implement data quality monitoring, validation, governance, responsible AI, bias detection, performance evaluation, and ethical compliance frameworks.
- Develop documentation, coding standards, model versioning, experiment tracking, deployment procedures, integration protocols, and executive reports.
- Collaborate with engineers, vendors, government agencies, academic partners, testing teams, and technology partners to deliver ML capabilities and optimize integrations.
Requirements
- Bachelor's degree in Computer Science, Data Science, Machine Learning, or a related technical field is required; a master's degree is preferred.
- Minimum 7–10 years of experience in MLOps, DevOps, or related systems engineering roles.
- Strong expertise with TensorFlow, PyTorch, Scikit-learn, Docker, Kubernetes, and cloud platforms.
- Experience with Apache Airflow, Kubeflow, MLflow, Spark, and Hadoop.
- Knowledge of government compliance requirements, security frameworks, responsible AI principles, and bias detection methodologies.
- Preferred qualifications include defense or intelligence community AI/ML experience, AWS ML, Azure AI, or GCP ML certifications, real-time data processing and streaming experience, and familiarity with DoD enterprise architecture and data-sharing standards.
- Current security clearance or ability to obtain the required clearance level.
Benefits
- Competitive compensation with bonus opportunities.
- Employer-paid health care.
- Training and development funds.
- 401(k) match.
Tech Stack
Apache AirflowApache HadoopApache SparkAWSAzureDockerGoogle Cloud PlatformKubernetesMLflowPyTorchscikit-learnTensorFlow
Categories
