Atyeti Inc
Lead Data Scientist
United States
Sponsorship not specifiedDetected 25 days ago
SQLVector DatabasesAWSAzureCloud PlatformsMachine LearningTensorFlowPyTorchscikit-learnData ScienceLLMsRAGStatisticsResearchCommunicationProblem Solving
About the role
- Mentor junior data scientists and contribute to AI best practices within the team.
- Strong hands-on experience in Artificial Intelligence and Machine Learning model development.
Responsibilities
- Develop predictive, classification, recommendation, and optimization models using structured and unstructured data.
- Build and fine-tune Large Language Models (LLMs) and Generative AI applications for enterprise use cases.
- Develop Retrieval-Augmented Generation (RAG) solutions and prompt engineering strategies.
- Collaborate with cross-functional teams including business stakeholders, data engineers, and application developers to deliver AI-driven solutions.
- Evaluate model performance and continuously optimize models for accuracy, scalability, and reliability.
- Document models, methodologies, and deployment processes following enterprise standards.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
- 6+ years of experience as a Data Scientist or in a similar AI/ML role.
- Hands-on experience with Large Language Models (LLMs) and Generative AI technologies.
- Experience with prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG).
- Experience with ML libraries and frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar.
- Experience with SQL and data manipulation techniques.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
Benefits
- Design, build, and deploy scalable AI and Machine Learning models to solve complex business problems.
Apply directly at Atyeti Inc →Create a free account for alerts like thisView Atyeti Inc immigration profile
This listing is sourced directly from Atyeti Inc's careers page and normalized into a canonical job model.