S

Siemens

Automation, digitalization, and smart infrastructure provider

AI Native Backend Engineer - Enterprise AI Agent Development Platform

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeShenzhen, Guangdong Province, ChinaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or a related major is required.
  • At least 3 years of backend development experience is required; practical LLM application or AI Agent development experience is preferred.
  • Strong proficiency in at least one backend language, Python or Go, and sound code design, data structures, algorithms, and troubleshooting skills are required.
  • Experience applying underlying AI capabilities to build business applications and delivering scenario-specific applications using Agent or Workflow capabilities is required.
  • Familiarity with REST APIs, asynchronous programming, database design, caching, message queues, and common backend service architectures is required.
  • Understanding of LLM fundamentals and practical development experience with model APIs, prompts, retrieval-augmented generation, tool calling, or Agent workflows is required.
  • A foundation in distributed systems and concurrent programming, including handling timeouts, retries, idempotency, task state, and error recovery, is required.
  • Familiarity with Git, Linux, Docker, common development workflows, unit and integration testing, and code review is required.
  • Ability to independently design, develop, test, and troubleshoot core modules is required.
  • Ability to package model capabilities as stable, reusable services and to model applications, orchestrate business processes, and deliver solutions for real business scenarios is required.
  • Understanding of reliability design for AI execution, including state management, error recovery, and result validation, is required.
  • Ability to optimize performance and costs across call chains, token consumption, context length, concurrency, and resource usage is required.
  • Ability to work from Shenzhen and meet team collaboration requirements is required.

Responsibilities

  • Develop core backend capabilities for the XAgent enterprise AI Agent development platform, translating AI Native architecture into runnable, testable, and deployable engineering capabilities.
  • Develop Agent Runtime modules for task execution, state management, execution-step orchestration, asynchronous task processing, pause and resume, timeout control, and exception handling.
  • Develop unified model-access and invocation services, supporting multiple model adapters, model routing, parameter management, rate limiting, retries, fallback, and call records.
  • Develop backend capabilities for Agent execution context, conversation state, memory storage, and knowledge retrieval.
  • Develop Workflow, single-Agent, and multi-Agent collaboration capabilities, ensuring execution is traceable, exceptions diagnosable, and results verifiable.
  • Design and implement AI applications based on underlying models and Agent engine capabilities, packaging applications, orchestrating business logic, and delivering scenario-specific capabilities for enterprise use cases.
  • Develop core modules for Skill and Tool registration, discovery, configuration, invocation, version management, and execution-result handling.
  • Support tool integration methods such as MCP, standardizing the packaging and invocation of third-party APIs, enterprise business systems, and industrial software capabilities.
  • Implement tool-call parameter validation, permission checks, timeout handling, exception recovery, and structured execution results.
  • Collaborate on tool-execution isolation, credential management, access control, and auditing to secure Agent execution.
  • Support integration of partner-developed Agents, Skills, and tools, resolving interface compatibility, execution stability, and version-upgrade issues.
  • Establish and improve logging, tracing, metrics collection, and exception alerting for model calls, Agent execution, and tool calls.
  • Help build Agent evaluation and regression-testing mechanisms to validate task completion, tool-call accuracy, output quality, and exception handling.
  • Analyze and optimize production issues such as hallucinations, tool-call failures, overly long contexts, and model timeouts.
  • Optimize prompts, context assembly, caching, concurrent calls, and asynchronous processing to reduce inference costs and response latency.
  • Write unit tests, integration tests, and automated test cases to ensure core modules are reliable and maintainable.
  • Collaborate with the traditional cloud-platform backend team to integrate unified authentication, tenant management, permissions, databases, message queues, and billing services.
  • Support collection of token-usage and Agent-task metering data for platform billing, cost analysis, and operations.
  • Participate in service deployment, performance testing, troubleshooting, and production operations in Docker and Kubernetes environments.
  • Follow shared code standards, branch management, code review, CI/CD, and release processes to continuously improve code quality.
  • Receive business requirements and deliver software functionality based on platform architecture and real business scenarios.

Desired Qualifications

  • Practical development experience with LangGraph, LangChain, or another Agent framework.
  • Familiarity with MCP Server and Client, tool registration and discovery, Function Calling, or multi-Agent collaboration.
  • Experience developing Agent Runtime, long-running task execution, state persistence, checkpoints, task recovery, or workflow engines.
  • Familiarity with vector databases, embeddings, reranking, knowledge-base retrieval, and context engineering.
  • Experience with LLM gateways, model routing, token metering, inference services, or AI cost optimization.
  • Familiarity with Kubernetes, Prometheus, and cloud-native deployment or observability technologies.
  • Experience with enterprise SaaS, multi-tenant platforms, developer platforms, or industrial-software integration.

About the Company

Siemens

Automation, digitalization, and smart infrastructure provider

Siemens is a global engineering and technology company focused on automation, digitalization, smart infrastructure, and mobility. Its products and solutions span industrial automation systems, electrical equipment, energy management, and digital services that help industries run more efficiently. The company’s core technology comes from its history of electrical generation and distribution, including the dynamo-electric principle, and it now sells hardware, software, and services that connect factories, buildings, grids, and transportation networks. Siemens differentiates itself through scale, a broad portfolio across the value chain, and a long track record of large-scale, multi-country projects in infrastructure and industry. Its goal is to enable smarter, more sustainable industrial processes and urban systems by integrating hardware, software, and digital services to improve productivity, energy efficiency, and mobility.

Company size
10,001+
Company stage
IPO
Headquarters
Munich, Germany
Founded
1847

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