Own end-to-end ML pipelines behind Cloudflare's internal AI agents and chatbots

Cloudflare's Data Intelligence & Analytics org is hiring an ML Engineer in Bengaluru to build and operate the pipelines behind internal AI-driven applications, agents, and chatbots used by GTM, engineering, and product teams. Expect deep questions on MLOps (Kubernetes, Airflow/Argo), full-stack delivery of ML services, and pragmatic GenAI/LLM integration work (vector databases, Workers AI, LangChain/LangGraph).

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What this interview tests

MLOps on Kubernetes (deploy/manage/support ML services)
End-to-end ownership: requirements to deployment to observability
Scientific computing in Python (scikit-learn, PyTorch/TensorFlow)
LLM/GenAI application building (LangChain/LangGraph, vector DBs)
Full-stack delivery across Python/React/TypeScript
Cross-functional partnership with Data Scientists/Engineers

Common question themes

Describe an ML application you deployed and operated on Kubernetes end-to-end

How do you approach MLOps tooling decisions (Airflow, Argo, CI/CD)

Tell me about a GenAI/LLM-powered agent or chatbot you built in production

How have you worked with Data Scientists to ship a model from training to inference?

How do you design for scale, reliability, and observability in a distributed data platform

How have you influenced architecture or mentored engineers on your team

How candidates describe it

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