Amazon Machine Learning Engineer II Intl Seller Growth 

United States, Washington, Seattle 

463004294

Come and be part of the International Seller Services (ISS) Central Analytics Data Engineering (DE) team and work on solving cutting edge GenAI solutions!
As a ML Data Engineer, you will provide technical expertise, lead ML engineering initiatives and build end-to-end analytical solutions that are highly available, scalable, stable, secure, and cost-effective. You carry projects from proof-of-concept to deployment and serving with a high standard for model maintenance and operations. You orchestrate complex and/or distributed modeling systems to unlock new ML capabilities for your customers.
You will have the opportunity to work with scalable model development tools using SageMaker, AWS, and Docker. You will bring in techniques for automating model training, evaluation, deployment and monitoring using machine learning pipelines. You will work closely with researchers in building algorithms ranging from classical machine learning to state-of-the-art deep neural network models on diverse types of signals.Key job responsibilities
- Design, implement and operate large-scale, high-volume, high-performance data structures for analytics and data science.
- Develop and deploy models and pipelines that scale
- Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions with a flexible and adaptable data architecture.- Identify opportunities in existing data/ML solutions for improvements
Example projects:- Setting up a Dev environment for experimenting multiple embedding models for RAG setup
-Implement a robust experimentation platform to test, iterate, and optimize the Conversation Assistants' performance across key RAG metrics
- Developing reusable cloud infrastructure and deployment patterns to accelerate productionalization

Experience with machine learning techniques such as pre-processing data, training and evaluation of classification and regression models, and statistical evaluation of experimental data.
2+ years of non-internship professional ML-software development experience
Programming experience with at least one modern language such as Python,Java, C++, or C# including object-oriented design
2+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
Experience in building production quality and large scale deployment of applications related to NLP and ML

Advanced knowledge of performance, scalability, enterprise system architecture, and engineering best practices
Academic and/or industry experience with one of more of the following domains: computer vision, deep learning, machine learning or large-scale distributed systems.