Principal Machine Learning Engineer (MLE)
Tasks
- Apply NLP fundamentals to production systems
- Architect LLM-powered systems
- Build classical machine learning models
- Build computer vision and NLP applications
- Build end-to-end machine learning pipelines
- Conduct A B testing and measure model performance
- Containerize and deploy machine learning services
- Deploy and manage models in production
- Design machine learning solutions for production
- Develop and deploy large language model solutions
- Implement MLOps best practices
- Implement and maintain model versioning experiment tracking and retraining
- Optimize machine learning workflows for performance scalability reliability and cost
Perks/Benefits
Skills/Tech-stack
AWS | App Engine | Attention Mechanisms | Automated retraining | Azure | CI/CD | Chatbots | Classification | Clustering | Computer Vision | Deep learning | Docker | Embeddings | Experiment tracking | Feature Engineering | GCP | Generative AI | Google App Engine | Image Segmentation | Image classification | Information Retrieval | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Training | Model versioning | NLP | Natural Language | Natural Language Processing | Object Detection | PyTorch | Python | Question Answering | Regression | Sentiment Analysis | Summarization | TensorFlow | Transformers | YOLOv7
Education
Regions
Countries
States
Cities
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