Join Us

Role Goal

Design and engineer enterprise-grade AI Agent platforms.

Responsibilities

• Lead overall platform architecture design, including module boundary definition, microservice decomposition, API contract design, data flow design, and technology evolution roadmap;
• Design and implement multi-tenant isolation, access control, security compliance, and audit systems to meet enterprise customers' stringent data security and privacy requirements;
• Build Kubernetes, Docker, CI/CD, monitoring, alerting, and automated operations infrastructure to ensure platform stability and scalability under high-concurrency, large-data scenarios;
• Build a unified LLM invocation gateway supporting routing, retry, fallback, caching, rate limiting, token metering, and cost tracking for cutting-edge model APIs;
• Lead selection and implementation of core components including API gateway, microservice framework, message queues, databases, and caching; proficiency in Kafka, PostgreSQL, Redis, and similar infrastructure;
• Write core code with a hands-on mindset; deliver runnable, monitorable, and scalable platform versions quickly with limited resources;
• Collaborate closely with data engineers to ensure platform architecture supports data ingestion, batch-stream integration, data warehouse modeling, vectorization pipelines, and RAG data layers.

Requirements

• 1-3 years of backend or platform development experience; candidates from tier-2 internet companies, mid-size firms, or startups preferred; big-tech experience also welcome;
• Proficient in at least one of Java, Go, or Python; familiar with Spring Boot, Spring Cloud, or equivalent frameworks;
• Familiar with Docker, Kubernetes, CI/CD, and monitoring/alerting systems with hands-on deployment and operations experience;
• Familiar with Kafka, PostgreSQL, Redis, and API gateways; understands microservice architecture design principles and common pitfalls;
• Experience in microservice decomposition, multi-tenant isolation, access control, and security compliance; enterprise or government project experience preferred;
• Proven experience building platforms from 0 to 1; able to independently design technical solutions and drive implementation;
• Understanding of LLM API invocation, RAG fundamentals, and vector database concepts; ability to integrate AI capabilities into platform engineering (no model training required, but must understand AI engineering fundamentals);
• Excellent communication skills, strong sense of ownership, willing to write code at the front line; startup mindset preferred over big-tech titles;

Bonus Qualifications

• Experience with enterprise, smart city, digital government, or data middle platform projects;
• Familiarity with lakehouse technologies (Iceberg, Hudi, Delta Lake);
• Familiarity with metadata management tools (Apache Atlas, DataHub, OpenMetadata);
• Open-source contributions, technical blogs, or patents are a plus.

Role Goal

Build bridges across heterogeneous data sources for the enterprise AI Agent platform, transforming raw data into knowledge, features, and context ready for LLMs

Responsibilities

• Design and develop connectors for diverse data sources including relational databases, REST APIs, file systems, message queues, IoT devices, logs, and unstructured documents; support both batch ingestion and CDC-based real-time capture;
• Build batch-stream unified data pipelines using Kafka, Flink, Spark, DataX, etc.; implement real-time and offline ETL/ELT workflows ensuring stable data flow across ODS, DWD, DWS, and ADS layers;
• Complete data warehouse layered modeling, dimensional modeling, and metrics system construction; unify business definitions to support data services and AI applications;
• Build AI-oriented vectorization pipelines covering document parsing, text chunking, cleaning, embedding generation, vector ingestion, index updates, and version management; integrate with vector databases such as Milvus, Chroma, Weaviate, Pinecone, and pgvector;
• Construct the data layer for RAG and Agent systems, processing PDF, Word, HTML, scanned document OCR, and speech transcripts; support hybrid retrieval, re-ranking, metadata filtering, and prompt template management;
• Manage metadata and data lineage, collecting technical, business, and operational metadata; maintain version lineage at table-level, field-level, and for AI datasets;
• Define data quality rules, configure task alerting, SLA monitoring, data reconciliation, and anomaly detection to ensure AI applications rely on accurate, timely, and traceable data;
• Implement access control, data masking, audit logging, multi-tenant isolation, and sensitive information filtering to ensure sensitive data stays within boundaries and complies with security requirements for financial and government clients;
• Perform SQL tuning, job optimization, storage compression, resource scheduling, and cost control; for LLM invocations, optimize token consumption, caching strategies, and batch processing to run AI capabilities at stable, controlled costs;
• Collaborate closely with backend engineers to produce technical documentation, data standards, API specifications, and operations manuals.

Requirements

• Expert in SQL; proficient in at least one of Python, Java, or Scala;
• Familiar with Spark, Flink, Kafka, and DataX; understands batch-stream unified design principles;
• Experienced with PostgreSQL, ClickHouse, StarRocks, and similar databases with hands-on tuning expertise;
• Proven experience in data warehouse layering, dimensional modeling, and metrics system construction; able to independently complete domain modeling;
• Understanding of metadata, lineage, data quality, and access masking; hands-on data governance experience;
• Familiar with embeddings, vector databases, RAG, LangChain, or LlamaIndex; understands LLM API invocation, prompt engineering, and token cost control;
• Experience in unstructured data processing, knowledge base construction, or semantic retrieval preferred;
• Strong sense of ownership, good documentation habits, able to collaborate across teams, and willing to learn and grow quickly in a startup environment;

Bonus Qualifications

• Experience with enterprise, government, smart city, or data middle platform projects;
• Familiarity with lakehouse technologies (Iceberg, Hudi, Delta Lake);
• Familiarity with metadata management tools (Apache Atlas, DataHub, OpenMetadata);
• Proven experience building data platforms from 0 to 1;
• Hands-on RAG, knowledge base, Agent, or LLM application deployment experience;
• Open-source contributions, technical blogs, or patents are a plus.

Role Goal

Drive engineering delivery of AI Agent products and build high-performance consumer applications.

Responsibilities

• Develop vertical AI Agent applications based on LLM stacks (e.g. LangChain, Qwen);
• Integrate LLMs/open-source models into product workflows and deliver production-ready modules;
• Design complex Agent workflows and optimize prompt engineering and fine-tuning processes;
• Own end-to-end AI application deployment and track LLM advances (Function Call, multimodal interaction);
• Build Agent evaluation and observability systems (tracing, tool-call chain analysis) to drive quality iteration;
• Design and iterate Agent memory and personalization for cross-session understanding and long-term companionship.

Requirements

• Recent bachelor's / master's / PhD graduates, or within 2 years of graduation;
• Bachelor's degree or above; Computer Science, Software Engineering, Machine Learning preferred;
• Strong proficiency in Python;
• Familiarity with the full LLM application development lifecycle; hands-on LangChain experience preferred;
• Able to independently debug Agent anomalies (tool-call loops, context overflow, hallucinations, etc.);
• Engineering-level understanding of prompt engineering (few-shot, chain-of-thought, structured output).

Role Goal

Design and implement the core engineering architecture and full-stack stack that powers AI Agent products, ensuring high performance, availability, and scalability.

Responsibilities

• Own overall technical architecture for AI financial agent products; lead full-stack development and integration of backend, frontend, and middleware;
• Build and maintain high-performance, low-latency real-time interaction systems for 24/7 stability;
• Develop and optimize data pipelines and knowledge systems to support industry analysis and report generation;
• Engineer and service-ize AI capabilities (model API orchestration, agent memory management) with clean, stable interfaces;
• Handle cloud deployment, monitoring, and performance optimization for large-scale user traffic;
• Build end-to-end monitoring, alerting, and debugging toolchains for production observability;
• Design async task scheduling and concurrency control for multi-user stability.

Requirements

• Recent bachelor's / master's / PhD graduates, or within 2 years of graduation; CS / Software Engineering background;
• Solid CS fundamentals; strong Python skills; familiarity with modern frontend frameworks such as React Native;
• Familiar with cloud platforms (Tencent / Azure / Alibaba) and containers (Docker / Kubernetes); microservice experience preferred;
• Strong software engineering practices; PostgreSQL design and tuning (indexes, connection pools, query optimization); API design; vector DB (pgvector) experience preferred;
• Basic understanding of AI / LLM applications; hands-on integration experience preferred; fintech interest preferred;
• Strong systems thinking and collaboration; able to own complex modules end-to-end;
• Able to independently triage production issues (pool exhaustion, cron races, WebSocket disconnects, etc.).

Internship Duration

≥3 months, ≥3 days/week

Responsibilities

• Assist in developing vertical AI Agent applications with LLM stacks (e.g. LangChain);
• Help integrate LLMs into product workflows and design Agent workflows / prompt engineering;
• Participate in AI application deployment and testing; track LLM advances (Function Call, multimodal);
• Help build Agent evaluation and observability (tracing, tool-call analysis);
• Contribute to Agent memory and personalization systems for long-term companionship.

Requirements

• Currently enrolled in bachelor's or master's programs; CS / Software Engineering / ML preferred;
• Proficiency in Python with practical project experience;
• Strong interest in LLM application development; LangChain experience preferred;
• Awareness of common Agent failure modes (tool-call loops, hallucinations) and debugging approaches;
• Basic understanding of prompt engineering (few-shot, chain-of-thought, structured output).

Internship Duration

≥3 months, ≥3 days/week

Responsibilities

• Support day-to-day product and operations work, and help write operation reports;
• Assist with user research and requirement analysis for vertical AI Agent products;
• Design growth strategies and operate communities / content platforms (e.g. Xiaohongshu);
• Analyze product usage data, produce reports, and propose optimizations.

Requirements

• Any major; Marketing / Psychology / Computer Science preferred;
• Passion for AI products; strong communication and writing skills;
• Data-sensitive, communicative, and self-driven; proficient in Excel / PPT;
• Experience in social media operations or user research preferred.

Internship Duration

≥3 months, ≥3 days/week

Responsibilities

• Contribute to product positioning and value proposition refinement;
• Help design early growth strategies and operate domestic / overseas communities and content platforms;
• Write product introductions, feature docs, and external communication materials;
• Organize online / offline branding events and explore partnerships;
• Track conversion metrics and propose optimizations.

Requirements

• Currently enrolled in bachelor's or master's programs; Advertising / Communications / Psychology preferred;
• Interest in AI products and fintech; familiarity with content platforms preferred;
• Strong bilingual (Chinese / English) content skills;
• Experience in social media, growth, or marketing preferred.