AI/ML Engineer II
Tasks
- Build and maintain MLOps pipelines for training deployment monitoring
- Conduct model evaluation validation and benchmarking
- Deploy and manage AI applications in cloud environments
- Design develop fine tune and evaluate machine learning models
- Document AI ML workflows architectures and lessons learned
- Ensure responsible data handling transformation and validation
- Implement production deployment and model lifecycle management
- Integrate AI capabilities into products and internal platforms
- Maintain auditable production data pipelines and AI workflows
- Monitor models in production and handle drift degradation and failures
- Optimize model performance for accuracy latency scalability and cost
- Perform data preprocessing feature engineering and augmentation
- Translate business requirements into technical architectures and model designs
- Troubleshoot AI ML issues across development and production
- Work with structured and unstructured healthcare and life sciences datasets
Perks/Benefits
- N/A
Skills/Tech-stack
Amazon Web Services | Azure | Cloud platform | Data Preprocessing | Deep learning | Docker | Feature Engineering | Fine Tuning | Google Cloud | Google Cloud Platform | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Fine-tuning | Model Monitoring | Natural Language | Natural Language Processing | Python | SQL | Web Services
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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