ML Ops Engineer
Tasks
- Build model APIs and services
- Build train validation test pipelines
- Conduct experiment design and evaluation
- Create inference and monitoring workflows
- Deploy machine learning solutions to production
- Develop machine learning models
- Implement reproducible ML workflows
- Package models for production
- Perform data exploration and preprocessing
- Perform error analysis and model iteration
Perks/Benefits
Skills/Tech-stack
AWS | Airflow | CI/CD | DBT | Data Leakage Prevention | Data leakage | Deep learning | Docker | Embedding pipelines | Experiment design | Feature Engineering | Feature Stores | GRPC | GitLab CI | HTTP APIs | Language Models | Large Language Models | Leakage prevention | MLflow | Machine Learning | PyTorch | Python | Retrieval-Augmented Generation | SQL | Scikit-learn | Spark | Statistics | TensorFlow | Vector Databases | Vector Search | Weights and Biases | XGBoost
Education
Roles
Data Scientist | Engineer | ML Engineer | MLOps Engineer | Scientist
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