Senior Customer Engineer, Start Up AIActive€116K–€182K

Cloudflare
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The opportunity

The Customer Engineer (CE) is the core driver of our technical go-to-market strategy: a trusted technical advisor and primary architect of the customer journey. You will bridge the gap between technical strategy and business value.

What you'll do

  • Internet fundamentals: Deep understanding of how the Internet works, including OSI Model layers 1–7, BGP, GRE, TCP/UDP, DNS (authoritative and recursive), HTTP(S), and SSL/TLS.

  • Security: Expertise in the security of Internet properties, including WAF,: Bot Management, Firewalls, Rate Limiting, (M)TLS, Zero Trust, and SASE concepts.

  • Performance: Expertise in the performance of Internet properties, including: speed, latency, caching, video streaming, HTTP/2, and TLS 1.3.

  • AI Engineering: Solid grounding in AI model fundamentals (model size, context length, reasoning models, fine-tuning versus prompting, and matching the right model to the job). Familiarity with common agent patterns, including knowing when a simple workflow beats an agent, and with open agent frameworks and standards such as MCP. Understanding of how to evaluate and operate AI systems in production.

  • AI-Augmented Workflows: Familiarity with prompt engineering, building prototypes with coding agents, AI-assisted scripting and log analysis, and workflow automation to augment everyday CE workflows.

  • Coding/Scripting: Proficiency in Python, JavaScript, Bash, Lua, Go, or similar languages to write custom integration scripts, API connectors, and edge computing functions.

What they're looking for

  • Developer Platform Fluency: Experience designing, building, deploying or scaling applications using RESTful/GraphQL APIs, SDKs, webhooks, and developer tooling in cloud-native environments. Familiarity with infrastructure-as-code (Terraform, Pulumi), CI/CD pipelines, and observability for developer-led deployments.
  • Technical Enablement: Track record of creating and delivering technical enablement (workshops, code samples, documentation, or developer onboarding) that accelerates customer adoption of complex platforms.
  • Applied AI Knowledge: Hands-on experience with retrieval and knowledge systems (RAG, embeddings, reranking, agentic search), domain adaptation through lightweight fine-tuning, sandboxed execution environments for agents that write and run code, LLM orchestration, or model-access controls.
  • AI Traffic & the Agentic Web: Understanding of how AI crawlers and agents interact with internet-facing applications: protecting AI endpoints, managing bot and agent access, and exposing services to agents.