What AI Companies Really Look for in Data Engineering Candidates

AI large‑model companies and departments place a strong emphasis on candidates' project depth and personal background, expecting them to handle complex projects from day one without much onboarding time. Technical stack matching is more important than business domain experience.

Cutting‑Edge Technology Stack

These teams avoid technical debt and prefer the latest industry solutions. Mastery of lake‑house frameworks like Hudi or Paimon is often required, and many are moving toward multimodal data support within these ecosystems.

Object Storage Requirements

Large models consume diverse, non‑standard, multimodal data (images, video, audio), so extensive use of object storage is essential. Candidates should be familiar with cloud provider services such as OSS/COS and open‑source alternatives like MinIO or Ceph.

Vector Storage and Embeddings

Understanding vector databases for embedding storage and retrieval (e.g., Milvus) is crucial for similarity search and recall tasks.

Real‑Time Computing

Real‑time feature engineering is a common demand; proficiency with streaming platforms like Flink is often a strict requirement.

Overall, the role offers a premium salary premium at this stage, but expects candidates to hit the ground running with these advanced technologies.