Amazon

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About Amazon Web Services Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 105 Availability Zones within 33 geographic regions, with announced plans for 18 more Availability Zones and 6 more AWS Regions in Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.

AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and consistent support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow.

Amazon Web Services came to China in 2013, and has been relentlessly investing and expanding our infrastructure and business since then. Amazon Web Services launched its China (Beijing) Region (operated by Sinnet) in September 2016 and its China (Ningxia) Region (operated by NWCD) in December 2017. In 2019, Amazon Web Services added a new region in Hong Kong, making China the only country with three Amazon Web Services regions aside from the U.S. In 2022, Amazon Web Services launched Local Zone in Taipei. Amazon Web Services has also established an AI lab in Shanghai and two IoT labs in Shenzhen and Taipei. The Amazon Web Services Partner Network has thousands of Partners in China. Amazon Web Services has supported over 10,000 local startups and has provided cloud skills training to over 700,000 talents. Amazon’s first two utility-scale renewable projects—a solar farm and a wind farm—are also generating clean energy to the country’s grid.

Do you have a builder’s mentality where “show me” means more than “tell me”? Are you passionate about AI infrastructure, understand cloud architectures & platforms, and quick to pick up emerging technologies? Are you adept at working with Customers to experiment with innovative approaches and the validate the technical feasibility of solutions?

Generative AI is rapidly growing in importance. We're witnessing an increasing number of remarkable GenAI models and innovations—from AI infrastructure and intelligent customer service chatbots to operational data analysis, code generation, and multimodal implementations. Given the scale required for developing GenAI workloads, the cloud is an ideal place to build them, and Amazon Web Services is the leader in this market. We’re looking for someone passionate and deeply excited about this space. Someone who is devoted to helping IC customers understand how GenAI can make a big difference to their businesses.

Key job responsibilities

• As an AIML Specialist Solutions Architect (SA) in AI Infrastructure, you will serve as the Subject Matter Expert (SME) for providing optimal solutions in model training and inference workloads that leverage Amazon Web Services accelerator computing services. As part of the Specialist Solutions Architecture team, you will work closely with other Specialist SAs to enable large-scale customer model workloads and drive the adoption of AWS EC2, EKS, ECS, SageMaker and other computing platform for GenAI practice.
• You will interact with other SAs in the field, providing guidance on their customer engagements, and you will develop white papers, blogs, reference implementations, and presentations to enable customers and partners to fully leverage AI Infrastructure on Amazon Web Services. You will also create field enablement materials for the broader SA population, to help them understand how to integrate Amazon Web Services GenAI solutions into customer architectures.
• You must have deep technical experience working with technologies related to Large Language Model (LLM), Stable Diffusion and many other SOTA model architectures, from model designing, fine-tuning, distributed training to inference acceleration. A strong developing machine learning background is preferred, in addition to experience building application and architecture design. You will be familiar with the ecosystem of Nvidia and related technical options, and will leverage this knowledge to help Amazon Web Services customers in their selection process.
• Candidates must have great communication skills and be very technical and hands-on, with the ability to impress Amazon Web Services customers at any level, from ML engineers to executives. Previous experience with Amazon Web Services is desired but not required, provided you have experience building large scale solutions. You will get the opportunity to work directly with senior engineers at customers, partners and Amazon Web Services service teams, influencing their roadmaps and driving innovations.

About the team

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

If you are someone who enjoys innovating, likes solving hard problems and working on the cutting edge of technology, we would love to have you on the team.

Diverse Experiences

AWS 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.

Inclusive Team Culture

AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.

Mentorship & 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.

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.

#AWSGCR

BASIC QUALIFICATIONS

• 5+ years of hands-on experience optimizing AI infrastructure, with deep expertise in inference acceleration frameworks (e.g., vLLM, SGLang, TensorRT, etc.), model training and serving systems across PyTorch and TensorFlow ecosystems;
• Advanced proficiency in Nvidia GPU performance optimization techniques, including memory management, kernel fusion, and quantization strategies for large-scale deep learning workloads;
• Strong foundation in parallel computing principles with practical CUDA programming experience, emphasizing efficient resource utilization and throughput maximization;
• Demonstrated success implementing and tuning distributed AI systems leveraging modern frameworks like Megatron-LM and Ray, with particular focus on LLM deployment and horizontal scaling across GPU clusters.

PREFERRED QUALIFICATIONS

• First hand implementation experience of AI Infrastructure operation & optimization (Nvidia GPU Chips, Frameworks, Servers, Networking, Power etc.).
• Graduate degree in a highly quantitative field (Computer Science, Machine Learning, Operations Research, Statistics, Mathematics, etc.);
• Proficiency in performance optimization on Amazon Trainiums;
• Proficiency in kernel programming for accelerated hardware using programming models such as (but not limited to) CUDA
• Solid end-to-end hands-on development experience of deep learning algorithms related to Transformers;
• Experience with patents or publications at top-tier peer-reviewed conferences or journals.
• Past and current experience writing and speaking about complex technical concepts to broad audiences in a simplified format; strong written and verbal communication skills;

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.