Senior Cloud Architect, ML/AI

Job Overview

This role involves leading the design and implementation of production-grade ML and Generative AI solutions on AWS, acting as a hands-on expert and trusted advisor for customers running AI/ML workloads at scale. The position is part of a global Forward Deployed Engineering organization, focusing on either Field Engineering (pre-sales and GTM) or Delivery (install base and customer health), translating business problems into secure, reliable, and cost-efficient cloud architectures, and evolving AI/ML practices internally and with customers.

Responsibilities

• Lead discovery, architecture, and implementation for advanced ML and Generative AI workloads on AWS, including data, training, inference, and integration layers.
• Own the technical success of engagements by defining outcomes, making tradeoffs visible, and ensuring production-ready designs for security, reliability, performance, and cost.
• Provide opinionated guidance on GenAI architectures like Amazon Bedrock, SageMaker, and Q, integrating with customers’ existing systems and processes.
• For Field Engineering: Partner with sales teams to shape and win opportunities, serve as technical lead for POVs and CloudBuild engagements focused on AI/ML, and build technical narratives and demos for revenue-generating motions.
• For Delivery: Act as a named technical advisor for existing customers, lead proactive engagements to unblock AI/ML issues, and participate in account-team routines to align architectures with customer goals.
• Help evolve AI/ML usage by turning solutions into reusable patterns that influence the product roadmap.

Qualifications

• Expertise in leading production-grade ML and Generative AI solutions on AWS with multi-cloud awareness.
• Hands-on experience as a trusted advisor for scaled AI/ML workloads from discovery to deployment and optimization.
• Ability to translate complex business problems into secure, reliable, cost-efficient, and observable cloud architectures.
• Strong technical leadership in either pre-sales/GTM or delivery/customer health contexts.
• Familiarity with GenAI tools such as Amazon Bedrock, SageMaker, and Q.