Machine Learning Operations (MLOps) Engineer
RESEARCH PARK BUILDING 1 (ARLIS), United States
USD 150K-225K Senior-level Full Time
Tasks
- Automate testing validation and monitoring
- Build and maintain ML training evaluation deployment pipelines
- Design infrastructure for AI/ML workloads
- Design scalable ML pipelines
- Implement CI/CD workflows for ML systems
- Implement feedback loops and retraining strategies
- Implement model versioning for reproducibility
- Integrate DevSecOps practices into ML workflows
- Manage data pipelines and feature stores
- Monitor model performance drift and system health
- Operationalize ML models in secure production environments
- Support deployment in constrained or classified environments
- Translate experimental models into production systems
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Airflow | Automated testing | CI/CD | Cloud platform | CloudFormation | Data Governance | Data Pipelines | DevSecOps | Distributed Training | Docker | Explainability | Feature Store | GPU Acceleration | Google Cloud | Google Cloud Platform | Infrastructure as Code | Kubeflow | Kubernetes | Microsoft Azure | Model Monitoring | Model auditing | Model versioning | PyTorch | Python | TensorFlow | Terraform | “as-code”
Education
Bachelor of Arts | Bachelor of Engineering | Bachelor of Science
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