Do you want to help define the future of Go to Market (GTM) at AWS using generative AI (GenAI)?
AWS Worldwide Specialists Org (WWSO) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector.
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 be working directly with the most important customers (across segments) in the GenAI model training and inference space helping them adopt and scale large-scale workloads (e.g., foundation models) on AWS, model performance evaluations, develop demos and proof-of-concepts, developing GTM plans, external/internal evangelism, and developing demos and proof-of-concepts.
Key job responsibilities
You will help develop the industry’s best cloud-based solutions to grow the GenAI business. Working closely with our engineering teams, you will help enable new capabilities for our customers to develop and deploy GenAI workloads on AWS. You will facilitate the enablement of AWS technical community, solution architects and, sales with specific customer centric value proposition and demos about end-to-end GenAI on AWS cloud.
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. You will have the technical depth and business experience to easily articulate the potential and challenges of GenAI models and applications to engineering teams and C-Level executives. This requires deep familiarity across the stack – compute infrastructure (Amazon EC2, Lustre), ML frameworks PyTorch, JAX, orchestration layers Kubernetes and Slurm, parallel computing (NCCL, MPI), MLOPs, as well as target use cases in the cloud.
You will drive the development of the GTM plan for building and scaling GenAI on AWS, interact with customers directly to understand their business problems, and help them with defining and implementing scalable GenAI solutions to solve them (often via proof-of-concepts). You will also work closely with account teams, research scientists, and product teams to drive model implementations and new solutions.
You should be passionate about helping companies/partners understand best practices for operating on AWS. An ideal candidate will be adept at interacting, communicating and partnering with other teams within AWS such as product teams, solutions architecture, sales, marketing, business development, and professional services, as well as representing your team to executive management. You will have a natural appetite to learn, optimize and build new technologies and techniques. You will also look for patterns and trends that can be broadly applied across an industry segment or a set of customers that can help accelerate innovation.
This is an opportunity to be at the forefront of technological transformations, as a key technical leader. Additionally, you will work with the AWS ML and EC2 product teams to shape product vision and prioritize features for AI/ML Frameworks and applications. A keen sense of ownership, drive, and being scrappy is a must.
A day in the life
Drive end-to-end technical engagements with strategic customers, including foundational model builders, physical AI innovators, and enterprise architects, by deeply understanding their business context and designing tailored AWS-powered solutions. You will present compelling architectures to executive and technical stakeholders, then guide implementation teams through successful adoption. Beyond delivery, you will serve as a critical voice of the customer, collaborating directly with AWS service team leaders to surface product gaps and shape roadmap priorities, ensuring AWS continues evolving alongside the industry's most demanding and forward-thinking builders.
About the team
The Frameworks team specializes in computational workloads, performance evaluation, and optimization. We partner with foundation model builders, large-scale training customers, and physical AI innovators to solve complex challenges across the full ML stack. Our expertise spans hardware (GPUs, Custom Silicon), operating systems, communication libraries (NCCL, MPI), frameworks (PyTorch, NeMo, JAX), and models (Llama, Nemotron). We also support physical AI workloads involving real-time inference, robotics simulation, and sensor data pipelines. Additionally, we work with containers (Docker, Enroot), orchestrators (EKS), and schedulers (Slurm) to deliver scalable, high-performance solutions for the most demanding AI applications.