GenAI & Cloud ML Solutions Architect

Amazon Web Services (AWS); Arlington (VA); On-site; USD 131,000 - 178,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Job summary

Amazon Web Services, Inc. is seeking a Machine Learning Engineer in the ProServe team to design, evangelize, implement, and scale AI/ML solutions that meet customer needs and business objectives.

You will work directly with clients to scope use cases, select models, and ensure success across the project lifecycle. The role requires proficiency in generative AI, scalable architectures, and best practices, with collaboration across stakeholders.

Qualifications

• 3+ years of ML/statistical modeling and data analysis experience.
• 2+ years of cloud architecture and solution implementation experience.
• Experience with full software development lifecycle and DevOps practices.
• Experience in defining and creating benchmarks for assessing GenAI model performance.
• Experience applying quantitative analysis to solve business problems and cross-team projects.

Responsibilities

• Design, implement, and optimize complex AI/ML solutions on AWS for diverse customers.
• Develop and deploy machine learning and generative AI applications at scale.
• Collaborate with stakeholders to identify high-value AI/ML use cases and gather requirements.
• Provide technical guidance on responsible and cost-efficient AI/ML practices.
• Advise customers on latest AI/ML advancements and data strategy approaches.
• Mentor teammates, share artifacts, and prototype new technologies.

Skills

GenAI experience

ML model development

Experiment design

Education

Master's degree or above in STEM

Tools

AWS services

Serverless technologies

Cloud architectures

Job description

Amazon Web Services, Inc. is seeking a Machine Learning Engineer in the ProServe team to design, evangelize, implement, and scale AI/ML solutions that meet customer needs and business objectives.

You will work directly with clients to scope use cases, select models, and ensure success across the project lifecycle. The role requires proficiency in generative AI, scalable architectures, and best practices, with collaboration across stakeholders.