Senior Applied AI/ML Scientist
Tasks
- Conduct problem definition data exploration feature engineering model training validation deployment
- Develop and productionize machine learning solutions for document understanding
- Develop and productionize machine learning solutions for generative AI
- Develop and productionize machine learning solutions for search and question answering
- Develop and productionize machine learning solutions for virtual agents
- Develop data pipeline tools and processes for ML training
- Document and communicate research development and architectural decisions
- Ensure security and compliance of healthcare data under HIPAA
- Fine tune LLMs for healthcare domain
- Investigate LLM models and compare accuracy and efficiency
- Participate in code reviews testing and quality assurance
- Perform end to end ML lifecycle model development
- Troubleshoot and resolve AI model integration deployment and backend service issues
Perks/Benefits
Skills/Tech-stack
AWS SageMaker | Algorithms | Autoregressive Language Models | Azure Machine Learning | Data Pipelines | Databricks | Decision Trees | Deep Neural Networks | DeepSpeed | Design Patterns | Feature Engineering | Fine Tuning | HIPAA | Huggingface | Human Feedback | LLM Fine-tuning | Language Models | Large Language Models | Learning from Human Feedback | Machine Learning | Model Deployment | Model Training | Model Validation | Multimodal Language Models | Neural Networks | Object-Oriented | Object-oriented programming | Open Source | Open source LLM | PyTorch | Python | Quality Assurance | RAG | Reinforcement Learning | Reinforcement Learning from Human Feedback | Transformers
Education
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