ML Engineer
Tasks
- Build inference endpoints
- Build scalable machine learning pipelines
- Design machine learning models
- Detect data drift
- Develop automated testing and deployment pipelines
- Develop distributed data processing workflows
- Develop genai applications
- Implement MLOps best practices
- Maintain technical documentation and runbooks
- Monitor model performance
- Operationalize machine learning solutions
- Optimize cloud infrastructure with DevOps
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Azure | Azure ML | Batch Scoring | CI/CD | Data Drift | Data Drift Detection | Data Ingestion | Databricks | Docker | Drift Detection | Experiment tracking | Feature Engineering | Git | GitHub | Google Cloud | Kafka | Kubernetes | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Model Deployment | Model Explainability | Model Governance | Model Monitoring | Model Training | Model Validation | Model retraining | Model versioning | Prompt engineering | PySpark | Python | RAG | Real Time | Real-time Inference | Spark | Streaming | Vector Databases
Education
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