MLOps Engineer — AI/ML Systems & Deployment (TS/SCI Preferred)
Tasks
- Apply metadata standards
- Build production ML pipelines
- Containerize pipelines with Docker
- Deploy and manage Kubernetes ML workloads
- Deploy models to mission environments
- Design for reproducibility auditability and stability
- Develop runbooks and deployment standards
- Enable batch and real-time inference
- Enable data versioning and governance
- Implement model versioning and lineage
- Monitor model performance and drift
- Monitor system health and latency
- Orchestrate ML workflows
- Scale training and inference workloads
- Support feature engineering and dataset preparation
- Transition workflows to containerized pipelines
- Use Prometheus Grafana and OpenTelemetry
Perks/Benefits
- 401k match
- Dental insurance
- Disability insurance
- Health insurance
- Home office & equipment plan
- Life insurance
- Paid time off
- Paid training
- Vision insurance
Skills/Tech-stack
Apache Airflow | Argo Workflows | Batch inference | CI/CD | ClearML | Containerization | Data Governance | Data Versioning | Distributed Systems | Docker | Feature Engineering | Grafana | Jupyter | Kubeflow | Kubernetes | LakeFS | Learning operations | MLflow | Machine Learning | Machine Learning Operations | Model Drift | Model Monitoring | OCI | OpenTelemetry | Prometheus | PyTorch | Python | Real Time | Real-time Inference | TensorFlow
Education
N/A
Roles
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