Sr Worldwide Specialist Solutions Architect - Genai, Data & Ai Gtm
Amazon
United States
153,600.00 - 207,800.00 usd annually py
On-site
Genai model training and inference
Aws cloud platform
Ml frameworks pytorch, jax
Amazon is seeking a Senior Worldwide Specialist Solutions Architect for their GenAI, Data & AI Go-To-Market team, focused on driving customer adoption of generative AI solutions on the AWS platform. The role involves developing strategies, engaging with key customers, and collaborating with engineering teams to enhance AWS offerings in the AI space
Job Summary
You will be part of the core worldwide GenAI Training and Inference team, responsible for defining, building, and deploying targeted strategies to accelerate customer adoption of our services and solutions across industry verticals.
You will possess a technical and business background that enables you to drive an engagement and interact at the highest levels with startups, Enterprises, and AWS partners.
This is an opportunity to be at the forefront of technological transformations, as a key technical leader.
Matching Summary
Match Score: 85
Amazon is seeking a Senior Worldwide Specialist Solutions Architect for their GenAI, Data & AI Go-To-Market team, focused on driving customer adoption of generative AI solutions on the AWS platform. The role involves developing strategies, engaging with key customers, and collaborating with engineering teams to enhance AWS offerings in the AI space.
Salary
153,600.00 - 207,800.00 USD annually
Skills & Requirements
Must-have
GenAI model training and inference
AWS cloud platform
ML frameworks PyTorch, JAX
orchestration layers Kubernetes and Slurm
parallel computing NCCL, MPI
MLOps expertise
customer engagement and GTM planning
Nice-to-have
deep familiarity across the stack
articulate GenAI potential and challenges
drive product vision and feature prioritization
keen sense of ownership, drive, and scrappiness
Key Requirements
Technical and business background
Deep familiarity across compute infrastructure, ML frameworks, orchestration layers, parallel computing, MLOps