Frontier AI Technical Account Manager (Germany), Enterprise Support EMEA - Startups (AWS)

Amazon; Berlin; On-site; EUR 120,000 - 150,000; Full time

Job summary

Amazon Web Services (AWS) seeks an Enterprise Account Engineer II to serve as a trusted technical advisor to customers deploying Frontier AI workloads on the cloud. You will design cloud architectures, discuss operational trade-offs, and collaborate with solutions architects to remove blockers while helping customers scale AI initiatives and optimize performance.

This role combines cloud technology with AI innovation, guiding customers from foundation model training to production inference in a

Qualifications

• Experience with operational parameters and troubleshooting for three or more domains in a distributed systems environment.
• Experience in technical engineering.

Responsibilities

• Design cloud architectures optimized for Frontier AI workloads, including training, fine-tuning, and inference across GPU environments
• Drive technical discussions on operational trade-offs, incident management, and risk mitigation for AI pipelines
• Collaborate with solutions architects, service engineering, and account managers to resolve blockers for AI customers
• Review customer environments to improve resilience, scalability, and cost efficiency with performance targets
• Deliver guidance through architecture reviews, workshops, and enablement sessions to integrate cloud and AI best practices

Skills

Operational troubleshooting across dom

Technical engineering

Education

Bachelor's degree or equivalent

Job description

Frontier AI Technical Account Manager (Germany), Enterprise Support EMEA - Startups

Job ID: 10566486 | AWS EMEA SARL (Germany Branch)

Are you passionate about helping organizations push the boundaries of artificial intelligence? 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.

Key job responsibilities

• 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

A day in the life

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.

About the team

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.

Basic Qualifications

• Experience with operational parameters and troubleshooting for three (3) of the following: compute/storage/networking/CDN/databases/DevOps/big data and analytics/security/applications development in a distributed systems environment
• Experience in technical engineering

Preferred Qualifications

• Experience with AWS services or other cloud offerings
• Experience in internal enterprise or external customer-facing environment as a technical lead
• Bachelor's degree or equivalent

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