Principal Machine Learning Engineer (MLE)
Tasks
- Analyze experiment results and improve models
- Apply NLP fundamentals for production systems
- Architect LLM-powered systems
- Build end-to-end machine learning pipelines
- Conduct model monitoring and A B testing
- Containerize machine learning services and deploy
- Deploy machine learning and LLM applications
- Design machine learning solutions for production
- Implement MLOps best practices
- Implement experiment tracking and model versioning
- Optimize machine learning workflows for performance scalability reliability and cost
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AWS | Attention Mechanisms | Automated retraining | Azure | B testing | CI/CD | COCO | Cityscapes | Computer Vision | Docker | Embeddings | Experiment tracking | GCP | Generative AI | Image classification | Information Retrieval | Kubernetes | Language Model | Large Language Model | MLOps | MLOps CI/CD | Machine Learning | Model versioning | NLP | NLP Text Summarization | Object Detection | PyTorch | Python | Semantic Segmentation | Statistical Testing | TensorFlow | Text Summarization | Text preprocessing | Transformers | YOLOv7
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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