Senior Cloud Architect, Field Engineering – GenAI Focus

Job Description

Join our team to deliver high-impact AI engagements, leading hands-on GenAI implementation projects and translating customer goals into practical architectures. You will build and validate AWS-native AI and data solutions, owning technical execution from discovery through delivery. Your role involves driving product adoption, contributing to new logo acquisition, and expanding the install base. You'll identify reusable assets, standardize engagement sizing, and partner with various teams to ensure well-scoped, documented customer engagements tied to clear success criteria. Maintain clear visibility into active work, risks, and dependencies.

Qualifications

1. Experience in customer-facing cloud architecture, technical consulting, solutions delivery, or field engineering. 2. Hands-on experience with AWS in real customer environments. 3. Working knowledge of modern AI and GenAI architectures on AWS — particularly Amazon Bedrock (Knowledge Bases, model evaluation, guardrails), retrieval-augmented generation (RAG) patterns with vector databases, and agentic AI design patterns. 4. Ability to move between technical depth and customer-facing communication with ease. 5. Experience leading workshops, discovery sessions, implementation activities, or technical POVs. 6. Strong judgment in ambiguous environments; able to simplify, prioritize, and move work forward without heavy process overhead. 7. Comfortable working across sales, delivery, customer success, product, and partner stakeholders. 8. Natural ownership mentality: escalate early, resolve fast, and own the outcome. 9. Bonus Points: Experience delivering GenAI workshops, technical assessments, or customer implementation engagements. 10. Experience with the AWS Migration Acceleration Program (MAP), partner-funded implementation programs, or similar structured cloud adoption programs. 11. Experience building reusable technical assets, templates, or playbooks that improved delivery leverage. 12. Experience with Amazon SageMaker for MLOps workflows, model monitoring, or custom model deployment. 13. Familiarity with agentic AI frameworks (e.g., AgentCore, Strands, or similar orchestration tools). 14. Hands-on experience with vector databases (Aurora pgvector, OpenSearch) in production RAG architectures. 15. AWS cloud certifications. 16. Experience with DoiT products, cloud cost optimization, Kubernetes, data engineering, or platform modernization.

Benefits

- Unlimited Vacation - Flexible Working Options - Health Insurance - Parental Leave - Employee Stock Option Plan - Home Office Allowance - Professional Development Stipend - Peer Recognition Program