Senior Data Scientist

Job Title: Senior Data Scientist Customer Location: Los Angeles, CA Hire type: Contract W2 Technical Skills: · Advanced Python development for ML/AI workloads · End‑to‑end ML lifecycle: model training, evaluation, fine‑tuning, and labeling/tagging workflows · Generative AI systems design, including LLM-based application development · Prompt engineering optimization for large language models · Document AI pipelines: OCR/extraction, parsing, normalization, and text chunking for structured & unstructured data · Embedding generation pipelines for semantic search and retrieval · Vector similarity search implementation using vector databases · ML model integration with Vector DBs and MongoDB · Production‑grade ML engineering: scalable, maintainable, and deployment‑ready code · Knowledge of CI/CD pipelines and cloud deployment (Azure preferred) · Experience with Vector DBs and/or MongoDB Python, Large Language Models (LLMs) (via LLM‑based applications), Vector Databases, MongoDB Roles & Responsibilities 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. Strong fit if the candidate: · Has expert-level Python skills · Has hands-on experience building ML/GenAI systems, not just theoretical knowledge · Has worked on end-to-end ML pipelines (data → model → deployment) · Has experience with document AI, embeddings, and vector search · Thinks like an engineer (scalable, maintainable, production-ready code) Key Responsibilities · 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