Principal ML Analytics Engineer
Tasks
- Apply DevSecOps deployment pipelines
- Apply data first problem solving
- Build MLOps pipelines for model deployment and updates
- Build feature engineering pipelines
- Collaborate with data scientists and business stakeholders
- Design develop deploy test analytics models and AI applications
- Detect and mitigate bias
- Develop APIs for predictions and model access
- Follow Agile SDLC practices
- Implement model monitoring
- Implement training pipelines
- Integrate AI services with existing applications
- Integrate models into production environments
- Maintain optimize models
- Mentor engineers
- Present model insights and performance recommendations
- Train evaluate machine learning models
- Write model specifications for reproducibility and maintenance
Perks/Benefits
- N/A
Skills/Tech-stack
AIOps | API Integration | Agile | Amazon SageMaker | Authentication | Azure Machine Learning | Bias detection | Cloud Data | Cloud data warehousing | Data Governance | Data Mining | Data Modeling | Data Quality | Data Security | Data Streaming | Data Warehousing | DataOps | Dataiku | DevSecOps | Ethical AI | Event Driven | Event-driven architecture | Feature Engineering | Generative AI | MLOps | Machine Learning | Model Monitoring | Model versioning | NoSQL | Python | R | Relational databases | SDLC | SQL | Statistical Methods | Testing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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