AI/ML Operations Engineer
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:
- 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.
AI/ML Operations Engineer
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:
- 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.
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