Amazon Machine Learning Engineer Generative AI Innovation Center AWS 

United States, Washington, Seattle 

212703766

GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud.As a Machine Learning Engineer in GAIIC, you are proficient in developing and deploying advanced ML models and pipelines to solve diverse customer problems using Gen AI. You will be working alongside scientists with terabytes of text, images, and other types of data and develop Gen AI based solutions to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.Key job responsibilities
- Solve complex technical problems, often ones not solved before, at every layer of the stack.
- Design, implement, test, deploy and maintain innovative ML solutions to transform service performance, durability, cost, and security.
- Build high-quality, highly available, always-on products.
A day in the life
As you design and code solutions to help our team drive efficiencies in ML architecture, you’ll create metrics, implement automation and other improvements, and resolve the root cause of software defects. You’ll also:- Participate in design discussions, code review, and communicate with internal and external stakeholders.- Work in a startup-like development environment, where you’re always working on the most important stuff.About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance
Sales, Marketing and Global Services (SMGS)

- 3+ years of non-internship professional software development experience
- 3+ years of programming with at least one software programming language experience
- 3+ years of design or architecture (design patterns, reliability and scaling) of new and existing machine learning systems experience
- 2+ years of relevant experience in developing and deploying large scale machine learning or deep learning models and/ or systems into production, including batch and real time data processing, model containerization, CI/ CD pipelines, API development, model training and productionizing ML models
- Experience using Python and frameworks such as Pytorch, Tensorflow

- Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
- Experience in training and fine-tuning of Large Language Models (LLMs), or experience with inference optimization
- Master’s degree in computer science or equivalent
- Experiences related to AWS services such as Sagemaker, EMR, S3, DynamoDB and EC2