Machine Learning Engineer (MLOps & AI Infrastructure)
Tasks
- Automate data preprocessing feature engineering and retraining
- Build reusable ML frameworks and internal tools
- Deploy and manage ML models using cloud services
- Design ML pipelines for data ingestion training and inference
- Develop scalable ML infrastructure architectures
- Enforce CI CD version control and automated testing
- Ensure ethical AI standards and data privacy compliance
- Implement alerting for production ML systems
- Integrate Generative AI solutions into ML workflows
- Monitor data drift model drift and performance degradation
- Operationalize ML models for production deployment
- Orchestrate ML model lifecycle with workflow tools
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS Glue | AWS Lambda | Amazon Athena | Amazon EMR | Amazon S3 | Amazon SageMaker | CI/CD | Data Drift | Data drift monitoring | Docker | Drift monitoring | Git | GitHub | GitHub Actions | Jenkins | Kubernetes | MLflow | Model Drift | Model drift monitoring | PySpark | PyTorch | Python | SQL | Sagemaker Pipelines | Scikit-learn | Spark MLlib | TensorFlow
Education
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