The Role
Designs, implements, and maintains MLOps infrastructure and pipelines for enterprise AI/ML systems supporting DIA. Manages end-to-end ML lifecycle including training, deployment, monitoring, automated retraining, model versioning, experiment tracking, GPU cluster management, and integration with DevSecOps and DIA compliance requirements.
Summary Generated by Built In
cFocus Software seeks a AI/ML Operations Engineer to join our program supporting the Defense Intelligence Agency (DIA). This position is on site in the Washington DC, MD, & VA area. This position requires a TS/SCI + CI Polygraph clearance.
Qualifications:
Duties:
Qualifications:
- Active TS/SCI + CI Polygraph clearance
- Bachelor's degree (Master's preferred) in Computer Science, Data Science, or related field with 4-12+ years of experience
- Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML)
- Proficient in containerization (Docker, Kubernetes) and orchestration for ML workloads
- Experience with model deployment, monitoring, and CI/CD for ML
- Knowledge of ML frameworks (TensorFlow, PyTorch) and model serving technologies
- Understanding of data engineering and feature engineering pipelines
- Experience with cloud ML platforms and infrastructure as code
Duties:
- Designs, implements, and maintains MLOps infrastructure and pipelines for enterprise AI/ML systems.
- Manages end-to-end ML lifecycle including model training, deployment, monitoring, and retraining.
- Implements automated ML pipelines, model versioning, and experiment tracking.
- Ensures ML systems are scalable, reliable, and performant in production environments.
- Integrates ML workflows with DevSecOps practices and maintains compliance with DIA QAF requirements.
- Monitors model performance, detects drift, and implements automated retraining workflows.
- Manages ML infrastructure including GPU clusters, feature stores, and model registries.
Skills Required
- Active TS/SCI clearance with CI Polygraph
- On-site work in the Washington DC/MD/VA area
- Bachelor's degree in Computer Science, Data Science, or related field
- Master's degree in a related field
- 4-12+ years of relevant experience
- Experience with MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML)
- Proficiency with containerization and orchestration for ML (Docker, Kubernetes)
- Experience with model deployment, monitoring, and CI/CD for ML
- Knowledge of ML frameworks and model serving (TensorFlow, PyTorch, model serving technologies)
- Understanding of data engineering and feature engineering pipelines
- Experience with cloud ML platforms and infrastructure as code
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The Company
