AWS ML Engineer

Data Analysis and Exploration: Analyze large, complex datasets to extract meaningful insights and identify trends. Perform exploratory data analysis (EDA) using AWS data processing tools. Model Development: Build, train, and evaluate machine learning models using AWS services such as Sage Maker, and frameworks like Tensor Flow. ETL and Data Preparation: Work with AWS Glue, Redshift, Textract and other data engineering tools to preprocess, transform, and manage data for machine learning purposes. Machine Learning Pipeline Development: Develop end-to-end machine learning pipelines on AWS to automate and operationalize the deployment of models at scale. Collaboration: Work closely with data engineers, business analysts, and stakeholders to understand business needs and tailor data science solutions to meet those needs. Model Deployment and Monitoring: Deploy models to production and set up monitoring systems to track performance, accuracy, and other key metrics. Use Sage Maker and Lambda for model hosting and API development. Documentation and Reporting: Document models, processes, and findings for stakeholders, enabling clear communication of results and decision support.