From Campus to Backend Engineer: 3 Years of Growth at JD

This article shares a recent graduate's three‑year journey at JD, offering practical advice for newcomers, detailing a large‑scale system redesign for a merchant conference, outlining promotion‑season preparation, and describing the development of an AI assistant while emphasizing continuous learning and professional mindset.

JD Retail Technology
JD Retail Technology
JD Retail Technology
From Campus to Backend Engineer: 3 Years of Growth at JD

Graduating from the University of Chinese Academy of Sciences in 2021, the author joined JD as a fresh graduate and has been working on merchant content, AI assistants, and backend services.

Personal Growth Journey

The author reflects on common challenges for new engineers—lack of recognition, unfamiliar codebases, and communication hurdles—and recommends building trust, taking initiative on small tasks, and gradually tackling more critical responsibilities.

When assigned few tasks, proactively investigate alerts, logs, and even front‑end work to demonstrate value.

If existing code seems flawed, consider incremental refactoring and document changes for future maintainers.

For blocked cross‑team communication, suppress emotions, practice empathetic listening, and ask clear, focused questions.

System Redesign for JD Merchant Conference

In July 2022 the team faced a live‑streaming demand that the seven‑year‑old system could not handle. A domain‑driven redesign was undertaken, focusing on:

Defining clear business boundaries to improve efficiency and maintainability.

Controlling change through unified terminology, business abstraction, and domain partitioning.

The effort involved extensive stakeholder discussions, DDD modeling, and three months of intensive development, testing, and iteration, ultimately delivering a stable platform for the conference.

Large‑Scale Promotion Preparation (618 & 11.11)

The team adopts a repeatable preparation cycle: review past events, identify bottlenecks, optimize critical paths, conduct stress tests, and perform post‑mortem analysis. Continuous improvement of monitoring, fallback mechanisms, and automated degradation safeguards ensures resilience during traffic spikes.

AI Assistant Development

Following the rise of ChatGPT in late 2022, the author’s group built a merchant AI assistant to address information overload, low‑quality chatbot responses, and fragmented workflows. Starting from zero, they formed a large‑model R&D team, created a modular platform, and iterated through three quarters to reach version 3.0 with gray‑release, permission control, rate limiting, and full‑stack observability.

Continuous Learning and Mindset

The author stresses the importance of humility, deep technical exploration (e.g., reading Tomcat source), and a disciplined learning routine—practice, think, and study. Embracing challenging tasks, such as mastering HTTP in real‑world scenarios, helps sustain growth beyond the early career stage.

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System ArchitectureBackend DevelopmentSoftware EngineeringDomain-Driven Designcareer adviceAI AssistantLarge‑Scale Deployment
JD Retail Technology
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