GenAI & ML Delivery Architect

Amazon; Dallas (TX); On-site; USD 154,000 - 208,000; Full time

Benefits offered by this job

Health insurance

RSUs

Sign-on payments

Job summary

Amazon seeks a Machine Learning Engineer within AWS ProServe to design, evangelize, and scale AI/ML solutions tailored to customer needs. You will train and fine-tune models, define strategies for GenAI deployments, and collaborate with stakeholders to deliver measurable business value.

You will lead technical guidance on responsible AI practices, develop scalable experiments, and mentor teammates while working across diverse projects that drive customer success in the cloud.

Qualifications

• 3+ years of cloud architecture and solution implementation experience.
• 5+ years of machine learning, statistical modeling, data mining, and analytics techniques experience.
• Experience in professional software engineering & best practices for the full SDLC.
• Experience in defining and creating benchmarks for assessing GenAI model performance.
• Experience translating complex technical solutions and data-driven insights into clear recommendations for diverse audiences.

Responsibilities

• Design and implement complex, scalable AI/ML solutions on AWS for customer needs.
• Develop and deploy ML models and GenAI apps that solve real-world problems at scale.
• Collaborate with stakeholders to identify high-value AI/ML use cases and requirements.
• Provide best-practice guidance on applying AI and GenAI cost-effectively and responsibly.
• Mentor and share knowledge; prototype new AI/ML tech within the team.

Skills

Cloud architecture

Machine learning

GenAI model benchmarks

Stakeholder communication

Tools

AWS services

CI/CD

Job description

Amazon seeks a Machine Learning Engineer within AWS ProServe to design, evangelize, and scale AI/ML solutions tailored to customer needs. You will train and fine-tune models, define strategies for GenAI deployments, and collaborate with stakeholders to deliver measurable business value.

You will lead technical guidance on responsible AI practices, develop scalable experiments, and mentor teammates while working across diverse projects that drive customer success in the cloud.