GenAI & ML Solutions Architect

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

Benefits offered by this job

Health insurance

401(k) matching

Paid time off

Parental leave

Job summary

Amazon Web Services, Inc. is seeking a Machine Learning Engineer to join the Professional Services (ProServe) team. You will work with customers to design, evangelize, implement, and scale AI/ML solutions that meet technical and business objectives, with a focus on Generative AI.

You’ll architect complex, scalable ML systems, train and fine-tune models, run experiments, and guide customers through AI transformation journeys, delivering value and best practices across the project lifecycle.

Qualifications

• 2+ years of cloud architecture and solution implementation experience.
• 3+ years of machine learning/statistical modeling and data analysis.
• Experience with software engineering best practices across the SDLC.
• Experience defining benchmarks for GenAI model performance.
• Experience solving business problems with data across cross-team projects.

Responsibilities

• Design, implement, and scale AI/ML solutions on AWS for customers.
• Develop and deploy ML models and GenAI apps, optimize performance at scale.
• Identify high-value GenAI use cases with stakeholders and gather requirements.
• Provide guidance on responsible and cost-effective AI practices.
• Mentor teammates, share knowledge, and prototype new AI technologies.

Skills

Cloud architecture

Machine learning

GenAI

Software engineering

Quantitative analysis

Job description

Amazon Web Services, Inc. is seeking a Machine Learning Engineer to join the Professional Services (ProServe) team. You will work with customers to design, evangelize, implement, and scale AI/ML solutions that meet technical and business objectives, with a focus on Generative AI.

You’ll architect complex, scalable ML systems, train and fine-tune models, run experiments, and guide customers through AI transformation journeys, delivering value and best practices across the project lifecycle.