Advisory AI Software Engineer
Role details
Job location
Tech stack
Job description
As a core engineering delivery role, you will be responsible for the end-to-end engineering implementation, global cloud deployment and full-stack system optimization of Lenovo's enterprise AI systems. Focused on back-end development with matched front-end technical capabilities, you will convert the AI agent system Reference Design (prototype/demo) into production-level, deployable products, undertake hybrid cloud/edge-cloud orchestration and deployment, and ensure the stable landing and large-scale operation of AI products across Lenovo's hybrid cloud, edge and end-device ecosystem. This role is the key link connecting AI prototype development and actual business application, and is critical to the engineering realization and global delivery of LATC's Hybrid AI and agentic computing strategy., Back-End Service Development: Based on the enterprise AI agentic system technical specifications and Reference Design, build enterprise-level distributed services using mainstream technical stacks; design and develop APIs and SDKs, implement core business logic, and ensure the high availability, high scalability and high performance of back-end services for AI agentic systems.
Full-Stack System Integration: Complete front-end interface development and human-computer interaction design for AI products (adapted to PC/mobile/cloud terminals); realize seamless data interaction and full-stack integration between front-end applications and back-end services, and ensure the fluency and usability of the entire system.
Hybrid Cloud & Edge-Cloud Deployment: Combine Lenovo's hybrid cloud technical architecture, complete containerization and orchestration of AI products based on Docker/K8s; implement global deployment on public cloud (AWS/Azure), private cloud and edge devices, and solve cross-regional deployment environment compatibility, network latency and resource scheduling problems.
System Performance Optimization: Optimize the running performance of AI products from the engineering level, including back-end service tuning, database query optimization, cache strategy design and front-end rendering optimization; reduce system latency, improve throughput, and adapt to the hardware performance characteristics of Lenovo's end devices/servers to achieve optimal running effect.
Deployment Documentation & Post-Delivery Maintenance: Compile detailed technical documents including product deployment manuals, operation guides and maintenance specifications; provide technical support for the post-delivery operation of enterprise AI products, track system real-time running status, and carry out version iteration and function optimization according to business needs and technical updates.
Requirements
- Minimum 5 years of Python backend development experience; proficient in asynchronous programming (asyncio) and high-concurrency I/O models.
- Hands-on experience building gateway services with FastAPI or Starlette; familiar with streaming output technologies including SSE, WebSocket and NDJSON.
- Skilled in Pydantic, type annotations and rigorous type checking workflows (mypy strict mode).
- Well-versed in Python engineering standards: pytest, asynchronous testing, packaging & release (pyproject), dependency and version management.
- Experienced in operation and maintenance of mainstream middleware, at minimum Redis and PostgreSQL; solid production knowledge covering connection pools, transactions & data consistency, index optimization, timeout & retry logic, idempotent design, fault recovery and data migration.
- Practical Docker containerization experience; capable of independently building images, optimizing runtime parameters and implementing environment isolation.
- Proficient with Docker Compose for building and maintaining multi-service local and test environments.
- Familiar with Linux development and deployment environments; able to write Shell scripts for automation of build, deployment, troubleshooting and log collection.
- Mandatory Vue frontend development skills; able to independently develop admin dashboards and debugging pages, conduct API joint debugging and troubleshoot issues.
- Strong system design and problem diagnosis capabilities, able to resolve complex cross-module and cross-service failures., * Familiar with LangGraph or equivalent graph orchestration frameworks; experience designing state machines or workflow engines.
- Production observability implementation experience, proficient in OpenTelemetry (traces, metrics, context propagation).
- Engineering experience building LLM applications, with deep understanding of multi-Agent orchestration, tool calling, structured output validation and correction mechanisms.
- Experience integrating vector search and memory modules (pgvector, ONNX Runtime, tokenizers, etc.).
- Hands-on experience with Langfuse or comparable evaluation & observability platforms.
- Multilingual development proficiency in Java / Python / C++.
- Experience maintaining open-source projects, including versioning strategies, compatibility maintenance, documentation and sample code development.