Principal Machine Learning Engineer (MLE)
Tasks
- Apply NLP techniques with transformers and embeddings
- Architect LLM powered agent systems
- Build end-to-end ML pipelines
- Collaborate on buy vs build decisions for generative AI
- Containerize ML services and deploy with Docker and Kubernetes
- Deploy and monitor models in production
- Design machine learning and LLM solutions for production
- Develop and train models with PyTorch and TensorFlow
- Develop classical ML and computer vision solutions
- Implement MLOps best practices
- Optimize ML workflows for performance scalability reliability and cost
- Run A B testing and analyze results
Perks/Benefits
- Disability insurance
- Employee assistance program
- Health insurance
- Life insurance
- Paid Holidays
- Paid time off
- Retirement plan
- Retirement savings plan
Skills/Tech-stack
A/B | A/B Testing | AWS | Attention Mechanisms | Automated retraining | Azure | B testing | CI/CD | COCO | Cityscapes | Classification | Clustering | Docker | Embeddings | Experiment tracking | GCP | Generative AI | Image classification | Kubernetes | LLM Agents | Language Models | Large Language Models | MLOps | Machine Learning | Model versioning | NLP | Object Detection | PyTorch | Python | Regression | Segmentation | Statistical Analysis | TensorFlow | Text preprocessing | Transformers | YOLOv7
Education
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