Data + AI + Systems
Ziven Liu
I'm currently learning and building at the intersection of data engineering, AI-native systems, and system design, gradually shaping my own way of understanding complex systems.
What keeps me interested is not only how a tool is used, but why it exists, where it belongs in a system, and whether it genuinely helps solve the problem at hand.
If I had to summarize it in one sentence, I'd say I'm trying to gradually place data systems, distributed foundations, and AI capabilities into one coherent engineering view.
Identity
Capability Model
I prefer to think of myself as an engineer still growing in multiple directions: data engineering is the backbone, system design gives me structure, and AI engineering keeps opening up new possibilities.
Data Engineer
Build reusable systems around pipelines, layered modeling, semantic definitions, and platform capabilities.
AI Engineer
Move beyond isolated experiments and wire features, retrieval, and inference into production workflows.
AI Native Builder
Explore how AI can reshape data modeling, quality diagnostics, and data interaction experiences.
System-Oriented Engineer
Care more about why components exist, how boundaries are drawn, and how systems evolve over time.
From Data To AI
System Graph
To me, this graph feels more like a learning map. Some parts come from hands-on practice, some are still areas I'm actively studying, but they are gradually connecting into one network.
Data Systems
These days I naturally think in terms of pipelines, modeling, and semantics, and I'm trying to make data systems clearer, steadier, and easier to reuse over time.
Distributed Foundations
Rather than only remembering how to configure things, I want to gradually understand the mechanisms and boundaries behind Kafka, Flink, Raft, and RocksDB.
AI-Native Infrastructure
I'm also learning how to connect RAG, recommendation systems, feature engineering, and Ray into real data platforms instead of leaving them at the demo stage.
Drag the nodes to see how these capabilities connect inside one system.
Trajectory
Evolution Path
This line is simply a record of a few stages that feel clear to me so far. There wasn't a dramatic leap, just a gradual push from study, internships, and work toward the intersection of data systems and AI.
Shanghai University of Finance and Economics
Data Science & Big Data Technology
Graduate Recommendation
Financial Information Engineering
ByteDance
Data Platform & Warehouse Practice
Zulution
AI Data Engineering
VAST · Tripo
AI Data Infrastructure
How I Work
Structured Thinking
Problems are naturally decomposed into layers, modules, and stages before execution.
First Principles
I tend to ask for the underlying reason behind protocols, storage, and consistency instead of memorizing conclusions.
System Design First
I care more about whether a whole system is scalable, observable, and maintainable than about isolated features.
Future-Oriented
I keep thinking about how AI will reshape next-generation data architecture instead of only optimizing local concerns.
Engineering + Product
When designing solutions, I think about system boundaries, user experience, and how ideas are communicated.
Tooling as Leverage
I use AI coding, CLI workflows, and automation to squeeze out repetitive work and spend more time on architecture.