Generative AI/ML Solutions Architect

Amazon Web Services (AWS); Atlanta (GA); On-site; USD 131,300 - 177,600; Full time

Job summary

Amazon Web Services (AWS) seeks a Machine Learning Engineer in the ProServe team to design, evangelize, implement, and scale AI/ML solutions for customers, applying Generative AI to real‑world problems with significant impact.

You will work with stakeholders to scope use cases, train and fine‑tune models, run experiments, and define scalable paths to AI adoption while upholding security, cost efficiency, and industry best practices.

Qualifications

• 2+ years of cloud architecture and solution implementation experience.
• 3+ years of machine learning/statistical modeling data analysis tools and techniques.
• Experience in professional software engineering & best practices for the full software development life cycle.
• Experience in defining and creating benchmarks for assessing GenAI model performance.
• Experience applying quantitative analysis to solve business problems and working on multi-team projects.

Responsibilities

• Designing and implementing complex, scalable, and secure AI/ML solutions on AWS tailored to customer needs.
• Developing and deploying machine learning models and generative AI applications that solve real-world business problems.
• Collaborating with customer stakeholders to identify high-value AI/ML use cases and requirements.
• Providing technical guidance on applying AI responsibly and cost-efficiently.
• Acting as a trusted advisor on AI advancements and diverse data sources for business impact.
• Mentoring and creating reusable AI/ML artifacts within the team.

Skills

Cloud architecture

Machine learning

Software engineering

GenAI benchmarking

Quantitative analysis

Education

Master's degree

Tools

AWS services

Job description

Amazon Web Services (AWS) seeks a Machine Learning Engineer in the ProServe team to design, evangelize, implement, and scale AI/ML solutions for customers, applying Generative AI to real‑world problems with significant impact.

You will work with stakeholders to scope use cases, train and fine‑tune models, run experiments, and define scalable paths to AI adoption while upholding security, cost efficiency, and industry best practices.