Storage Platform R&D Engineer(Database Intelligence)- rednote

JobCubby; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

JobCubby is seeking a senior backend engineer to lead the architectural evolution of a storage platform using AI-native technologies and LLM agents. You will own the technical roadmap for intelligent storage and automated operations, with emphasis on self-diagnostics, self-healing, and scalable auto-management.

You will collaborate across teams to integrate AI interaction paradigms and practical AI capabilities into the database platform, ensuring robust, product-focused delivery.

Qualifications

• Strong backend engineering foundation with Go/Java/Python.
• Hands-on experience with distributed storage systems.
• Proficiency in LLM agent development and AI-driven infra.

Responsibilities

• Lead architecture evolution of the storage platform with AI-Native tech.
• Build intelligent operations system for self-service DB management.
• Architect self-healing, self-optimizing automated operations.
• Drive AI adoption in database domains like SQL optimization and anomaly detection.
• Track and integrate cutting-edge AI agent products into the platform.

Skills

Go

Java

Python

Distributed storage

LLM Agent development

Tools

LangChain

LlamaIndex

OpenAI API

Claude API

Job description

to apply - email only, no card. You can also save this posting or score it againstyour profile with AI.## About the roleLead the architectural evolution of the storage platform by integrating AI-Native technologies and LLM Agents. Develop intelligent operations systems to automate database management, diagnostics, and self-healing processes.## RequirementsRequires strong engineering proficiency in backend languages and hands-on experience with distributed storage systems. Candidates must demonstrate expertise in LLM application development stacks and possess strong product-oriented technical leadership skills.## Full descriptionWhat you'll do1,Lead Storage Cloud Platform Architecture & Core Development - Drive the architectural evolution and core module development of Rednote's storage platform. Explore AI-Native architectures for intelligent storage solutions and transform the platform from a tooling-focused system into an intelligent platform.2,Build Intelligent Operations System - Own the technical planning and execution of a self-service storage and database management platform. Leverage large language model capabilities to build an intelligent operations agent that supports natural language interaction, lowering the barrier to database operations.3,Drive Intelligent Operations Transformation - Re-architect the automated operations platform based on LLM Agent technologies to build a self-diagnosing, self-healing, and self-optimizing intelligent operations system - enabling a shift from manual operations to fully automated, zero-touch operations.4,Deepen AI + Database Innovation - Lead practical LLM adoption in database domains, including: intelligent SQL optimization, anomaly detection and root cause analysis, capacity prediction and auto-scaling, and intelligent alerting noise reduction.5,Elevate Product Experience - Continuously track leading AI Agent products (such as Cursor, Claude Code, Devin, etc.) and integrate advanced AI interaction paradigms into the database storage platform to deliver industry-leading intelligent operations experience.Qualifications1,Strong Engineering Foundation - Proficient in at least one backend programming language (Go / Java / Python); familiar with frontend frameworks (Vue / React); hands-on experience building Agent applications using large model APIs (OpenAI, Claude, Qwen, etc.). 2,Deep Storage Domain Experience - Hands-on experience with distributed storage; familiar with the principles and operational practices of at least two of: distributed KV/cache, MySQL, distributed databases, graph databases, table storage, or object storage. 3,AI Application Development - Proficient in the LLM application development stack: RAG, Prompt Engineering, Function Calling, and multi-agent frameworks (LangChain, LlamaIndex, AutoGen, etc.); experience shipping complete projects. 4,Product Thinking & AI Fluency - Deep practical experience with AI tools such as Cursor, Claude Code, GitHub Copilot, etc.; ability to translate AI capabilities into product features and engineering delivery. 5,Strong Soft Skills - Excellent at breaking down requirements and driving technical execution; strong cross-team communicator; customer-oriented mindset and strong sense of ownership; forward-looking judgment on AI in infrastructure; fluent in both English and Chinese (spoken and written). 6,Bonus - A technical blog, open-source contributions, or AI competition awards are a plus; hands-on experience in AIOps, intelligent operations, or LLM Agent development is highly preferred.