Frontier AI Technical Account Manager

Who you are

• Experience in a similar role as a Technical Account Manager, Consultant, Solutions Architect, Platform Engineer, Systems Engineer, Cloud Architect etc
• Understand operational parameters and troubleshooting for 2 or more of the following: Compute, Storage, Networking, CDN, Databases, DevOps, Big Data and Analytics, Security, Applications Development
• Internal enterprise or external customer-facing experience with the ability to clearly articulate to small and large audiences * Ability to juggle tasks and projects in a fast-paced environment
• Experience of Operations of AI Workloads and leveraging AI for Operations
• Customer obsessed
• Professional experience with cloud offerings such as AWS, Azure, Google Cloud Platform etc
• Programming or scripting skills with a combination of Java, Python Perl, Ruby, C#, and/or PHP a plus but not a requirement
• Previous experience as a Software Engineer, Developer, DevOps Engineer etc. in a Startup or working with Startups would be beneficial
• Understanding of DevOps practices and tools including Continuous Integration / Deployment, Puppet, Docker, Kubernetes, Chef is a plus

What the job involves

• As an Enterprise Account Engineer II at Amazon Web Services (AWS), you will serve as a trusted technical advisor to customers building and scaling Frontier AI workloads on the cloud. Your deep understanding of cloud computing architecture, machine learning infrastructure, and large-scale distributed systems will help customers navigate complex challenges — from training foundation models to deploying inference endpoints at scale
• You will craft and execute strategies that accelerate your customers' AI initiatives, advising on architecture, operational readiness, and performance optimization. Whether your customers are training large language models, building generative AI applications, or scaling GPU-intensive compute clusters, you will be the technical expert they rely on to achieve their goals
• This is an opportunity to work at the intersection of cloud technology and AI innovation, where your recommendations directly shape how customers build the next generation of intelligent systems
• Design and recommend cloud architectures optimized for Frontier AI workloads, including large-scale model training, fine-tuning, and inference across GPU and accelerator-based compute environments
• Drive technical discussions on operational trade-offs, incident management, and risk mitigation for customers running complex AI and machine learning pipelines on AWS
• Collaborate with solutions architects, service engineering teams, and account managers to identify adoption opportunities and resolve technical blockers for AI-focused customers
• Review customer environments proactively to improve resilience, scalability, and cost efficiency, ensuring their AI infrastructure meets performance and availability targets
• Deliver technical guidance through architecture reviews, workshops, and enablement sessions that help customers integrate cloud and AI best practices into their operational processes
• You start your morning reviewing operational health dashboards for your customers' AI training clusters, checking for scaling events or service advisories that may need attention. Mid-morning, you join an architecture review with a customer's ML engineering team to evaluate their plan for deploying a new foundation model into production
• After lunch, you collaborate with an AWS service team to advocate for a feature request that would improve your customer's GPU utilization. Later, you prepare a quarterly business review that highlights progress on cloud maturity milestones and recommends next steps for optimizing their inference workloads
• Our team partners with customers who are at the forefront of AI innovation, helping them build and operate large-scale machine learning systems on AWS
• We work closely with service teams, solutions architects, and account managers to deliver a unified support experience. We are growing to meet increasing demand as more organizations invest in generative AI and foundation model development, and we are looking for collaborative, curious engineers who want to help shape how AI workloads run in the cloud