Sr Worldwide Specialist Solutions Architect - GenAI, Training & Inference

Sr Worldwide Specialist Solutions Architect – GenAI, Training & Inference

Do you want to help define the future of Go to Market (GTM) at AWS using generative AI (GenAI)?

AWS Sales, Marketing, and Global Services (SMGS) 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.

Within SMGS, 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.

About the team

The Foundation Models (fka Training & Inference) team is highly specialized on computational workloads, performance evaluations and optimization. We work with Foundation model builders and large scale training customers, dive deep into the ML stack including the hardware (GPUs, Custom Silicon), operating system (kernel, communication libraries (NCCL, MPI), Frameworks (PyTorch, NeMO, Jax) and models (Llama, Nemotron…). We also work with containers (Docker, Enroot), orchestrators (EKS) and schedulers (Slurm).

Diverse Experiences

Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

Inclusive Team Culture

Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

Mentorship and Career Growth

We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Minimum Requirements:

1. Bachelor’s degree in computer science, engineering, mathematics or equivalent
2. 8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience
3. 3+ years of design, implementation, or consulting in applications and infrastructures experience
4. 5+ years building or optimizing computational applications for large scale HPC systems (e.g. physics based simulations) to take advantage of high performance networking (e.g. Amazon EFA, Infiniband, RoCE), distributed parallel filesystems (e.g. Lustre, BeeGFS, GPFS) and accelerators (e.g. GPUs, custom-silicon)
5. Understanding of deep learning training and inference workloads and requirements for high performance compute, network and storage
6. 5+ years of infrastructure architecture, database architecture and networking experience
7. Experience working with end user or developer communities

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