Senior ML Engineer

Nasze wymagania: Advanced knowledge of Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic) Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions) Advanced knowledge of SQL and Data Modeling Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs Experience in experiment design (power analysis and hypothesis testing) Proficiency in both written and verbal communication, required for a remote and largely asynchronous work environment Demonstrated capacity to clearly and concisely communicate complex technical problems and propose iterative solutions Experience owning a feature from concept to production, including proposal, discussion, and execution Mile widziane: Experience with document processing tools (AWS Textract, Azure Document Intelligence, or similar OCR/layout detection systems) Experience with PDF and Image processing libraries (e.g. PyMuPDF, OpenCV, Pillow) Experience in Machine Learning/Data Science (e.g., ML algorithm selection, feature engineering, model training, hyperparameter tuning, supervised and unsupervised learning implementation, building model pipelines, using Machine Learning tools/libraries/frameworks) Experience working with AWS big data technologies (Redshift, S3, EMR, Glue, etc.) O projekcie: The role involves designing, implementing, and optimizing document intelligence pipelines and ML models for document classification, segmentation, and field extraction on AWS platforms. Zakres obowiązków: Design and implement end-to-end document intelligence pipelines on AWS Develop and optimize ML models for document classification, segmentation, and field extraction Build scalable data processing systems handling PDFs up to 2000 pages Collaborate with subject matter experts to create and refine requirements for extraction Own features from research through production deployment and monitoring Establish evaluation frameworks and quality metrics for extraction accuracy