About ai&
ai& is a new global AI technology company dedicated to meeting the world's growing demand for AI. Our vision is twofold: to serve as a premier AI lab specializing in localization, and to act as a global infrastructure and compute provider. We are building a unified, optimized global platform that integrates next-generation data centers and infrastructure, heterogeneous compute serving, and advanced model services. We believe that the most effective way to build and scale AI is to own the stack from top to bottom.
At ai&, we empower small teams with the autonomy needed to tackle significant challenges. Our approach is to deconstruct large problems into manageable components and solve complex issues collaboratively. We seek highly motivated, mission-driven individuals who demonstrate strong personal agency. We value curiosity as the foundation of talent, and we are looking for people eager to develop alongside our evolving technology and expanding business.
Role overview
As a Field Development Engineer for our Compute Platform, you are the technical partner that helps customers get the most out of ai's PaaS infrastructure. Our compute platform is built for simplicity — developers and engineering teams should be able to deploy web applications, APIs, backend services, and AI workloads without managing the infrastructure underneath. Your job is to make that promise real for every customer you work with.
You will work across a diverse customer base — from startups shipping their first production application to enterprises migrating complex workloads onto ai& infrastructure. You will own the technical relationship, architect the deployment, and stay close through go-live and beyond. You bring the platform expertise, the deployment patterns, and the hands-on technical depth to meet customers wherever they are and get them to production fast.
This is a peer-level technical role. You are not handing customers a documentation link. You are sitting with their engineering team, understanding their stack, and designing the solution together. When things break you diagnose them. When customers do not know the best path forward you show them one. And everything you learn in the field comes back into ai& to make the platform better.
Responsibilities
- Solution Architecture & Deployment Design: Design deployment architectures on ai's compute platform that fit the customer's application, team, and scale requirements. Own the solution from initial scoping through production deployment.
- Technical Customer Engagement: Lead technical onboarding, architecture reviews, and deep-dive sessions with customer engineering teams. Operate as a technical peer to developers, platform engineers, and CTOs at both startups and enterprises.
- Platform Onboarding: Get customers live on ai& compute quickly and correctly. Manage the technical onboarding process, resolve blockers, and ensure the first deployment experience sets the right foundation.
- Proof of Concept & Migration Support: Lead technical POCs for enterprise prospects. Help customers migrate existing workloads from other platforms onto ai& infrastructure, identifying risks early and managing the transition cleanly.
- Performance & Scalability Guidance: Advise customers on how to configure and optimize their deployments for performance, cost, and reliability. Help them understand how to scale on the platform as their usage grows.
- AI & ML Workload Support: For customers deploying AI and ML workloads, provide additional technical guidance on model serving, inference optimization, and integration with ai's broader AI platform capabilities.
- Technical Troubleshooting: Own issue resolution across the customer lifecycle. Diagnose problems at any layer — application, runtime, networking, infrastructure — and drive resolution quickly with internal engineering teams.
- Customer Feedback & Product Input: Capture the technical signal you gather in the field and bring it back to the product and engineering teams. The friction customers hit, the features they need, the patterns that keep coming up — all of it should flow back into how we build.
- Reference Architectures & Enablement: Produce reference architectures, deployment guides, and example configurations that help customers move faster and scale your expertise across the customer base.
You may be a fit if you have the following skills
- PaaS & Cloud Platform Experience: You have worked with PaaS or cloud infrastructure platforms — Heroku, Render, Railway, AWS Elastic Beanstalk, Google App Engine, or similar. You understand how developers deploy applications on managed platforms and where they run into trouble.
- Application Deployment Depth: Strong understanding of how web applications, APIs, and backend services are deployed and operated in production. You know about containerization, runtime environments, networking, databases, and the full stack that surrounds an application.
- Solutions Architecture Track Record: You have designed and delivered end-to-end technical solutions for customers in a customer-facing technical role. You think in systems, you own the architecture, and you see deployments through to production.
- Enterprise & Startup Customer Fluency: Comfortable working across both enterprise and startup environments. You adjust your approach depending on who you are talking to — a solo founder moving fast or a platform team at a large enterprise moving carefully.
- Troubleshooting Across the Stack: You can diagnose problems at any layer — application code, runtime configuration, networking, infrastructure. You do not need to hand off to understand what is happening.
- AI & ML Familiarity: Working familiarity with AI and ML workloads is a plus. You do not need to be an ML engineer but you should be comfortable having a technical conversation about model deployment and inference when the customer needs it.
- Relevant Tooling: Strong command of at least one backend language. Familiarity with containers, CI/CD pipelines, cloud networking, and deployment tooling. Python proficiency is a plus.
- Great Team Spirit: A mission-driven approach to engineering, valuing clear communication, hands-on execution, and collective success over individual silos.