MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred)
Tasks
- Apply metadata and data management standards
- Build and operate production machine learning pipelines
- Containerize pipelines with Docker
- Deploy and manage Kubernetes ML workloads
- Deploy models to secure environments
- Design for reproducibility and auditability
- Detect and resolve model drift
- Develop runbooks, playbooks, and documentation
- Enable batch and real-time inference
- Implement data versioning and governance
- Implement model versioning and lineage
- Monitor model performance and system health
- Orchestrate ML workflows
- Support feature engineering and dataset preparation
- Support scalable training and inference workflows
Perks/Benefits
- 401k match
- Covered certifications and training
- Home office equipment support
- Life insurance
- Long-term disability
- Medical, dental & vision coverage
- Paid time off
- Short-term disability
- Weekly pay schedule
Skills/Tech-stack
Apache Airflow | Argo | CI/CD | ClearML | Computer Vision | Data Governance | Data Versioning | Distributed Systems | Docker | Faster R-CNN | Feature Engineering | Grafana | Kubeflow | Kubernetes | LakeFS | Language Models | Large Language Models | MLflow | Machine Learning | OpenTelemetry | Prometheus | Python | R-CNN | Transformers | YOLO
Education
N/A
Roles
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