ML Data Science Engineer
Posted
Title: ML Engineering Data Science Engineer (hybrid)
Location: Woodland Hills, CA
Pay rate: $53/hr
Role Overview
Location: Woodland Hills, CA
Pay rate: $53/hr
Role Overview
- We are seeking a highly skilled Data Science Engineer to design and develop scalable ML and Generative AI solutions.
- The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.
- Data Science Engineer to design and develop scalable ML and Generative AI solutions.
- Python, hands-on experience in model training, document processing pipelines.
- Vector databases and modern ML/GenAI frameworks, deploy machine learning and GenAI solutions using Python Design.
- LLM-based applications Build document extraction, parsing, and chunking pipelines for structured and unstructured data Train, evaluate, and fine-tune ML models.
- workflows Implement embedding generation and vector search solutions Integrate ML models with Vector DBs and MongoDB Ensure code quality, scalability, and production readiness.
- Solid understanding of ML algorithms and Generative AI concepts Experience working with Vector Databases and/or MongoDB.
- Develop and deploy machine learning and GenAI solutions using Python Design and optimize prompt engineering strategies for LLM-based applications Build document extraction, parsing, and chunking pipelines for structured and unstructured data Train, evaluate, and fine-tune ML models; manage tagging and labeling workflows Implement embedding generation and vector search solutions Integrate ML models with Vector DBs and MongoDB Ensure code quality, scalability, and production readiness
- Expert-level proficiency in Python Strong experience in model training, evaluation, and tagging workflows
- Hands-on experience with document extraction and chunking techniques
- Solid understanding of ML algorithms and Generative AI concepts Experience working with Vector Databases and/or MongoDB
