Data Scientist

1. 3\+ years in ML/data science in enterprise settings. 2. DL: supervised/unsupervised learning, Segmentation, anomaly detection, model evaluation, feature engineering, Pytorch. 3. Programming: Python (expert) 4. Data: ETL/ELT familiarity, structured/unstructured data handling. 5. Tools: Git, VSCode, MLflow, Docker, Azure ML Studio, Azure DevOps Domain: Manufacturing/Quality inspection department preferred, but not a dealbreaker * Python engineering with strong development, maintenance/debugging, unit/integration testing, and CI/CD practices. * GenAI document extraction (prompting, evaluation), plus PDF/document parsing and text matching/validation, Azure OpenAI Services, VLM/OCR/layout models, ReqIF/XML handling, and DNG/DOORS import workflows; basic DevOps (containers, logging/monitoring). * Nice to have: Docker, Kubernetes, ReactJS, frontend development