AI Native Engineering-SAP
Tasks
- Analyze ML algorithms
- Apply AI best practices
- Build end-to-end data pipelines
- Create user stories
- Deploy AI ML platforms on Kubernetes
- Develop data augmentation pipelines
- Develop, validate, deploy AI models
- Establish development standards
- Estimate work plans
- Gather user requirements
- Rank solutions by success probability
- Share ML engineering best practices
- Tune hyperparameters
- Verify data quality
Perks/Benefits
- N/A
Skills/Tech-stack
AI ethics | API Security | API libraries | AWS | Auditing | Automl | Azure | Big Data | Cloud platform | Containerization | Data Augmentation | Data Modeling | Data Pipelines | Data cleaning | Distributed Systems | Explainable AI | Google Cloud | Google Cloud Platform | H2O | Hadoop | Hyperparameter Tuning | Kubernetes | LLM | LSTM | MLOps | Machine Learning | ModelOps | PySpark | Python | RNN | SSO | Scikit-learn | SparkML | TensorFlow | Trust | Version control | XGBoost
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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