Amazon Sr Machine Learning Engineer Global Services 

United States, Kansas 

766659614

Key job responsibilities
* Work closely with product, engineering, and science teams to design, build, and deploy end-to-end machine learning solutions
* Optimize and scale machine learning models for production environments
* Implement data pipelines and infrastructure for model training and serving
* Collaborate with data scientists and software engineers to integrate ML solutions into applications
* Monitor and maintain deployed ML models and solutionsAbout the team
Diverse Experiences
Amazon values diverse experiences. Even if you do not meet all of the preferred 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.Why AWSWork/Life BalanceInclusive Team CultureMentorship and 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.

- 6+ years of non-internship professional software development experience
- 6+ years of programming with at least one software programming language experience
- 6+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- 5+ years experience developing and deploying large-scale machine learning models and/or applications in production, including batch and real-time data processing, model containerization, CI/CD, REST or GraphQL APIs.
- Experience with Python and frameworks such as Pytorch, TensorFlow

- Bachelor's degree in computer science or equivalent
- 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Hands-on experience building models with deep learning frameworks like MXNet, Tensorflow, Keras, Caffe, PyTorch, or similar. Prior experience training and fine-tuning Large Language Models (LLMs)
- Experience with serverless, event-driven architectures in AWS, including system design, development, and production operations; and infrastructure-as-code using AWS CDK, CloudFormation, or Terraform.