The DCS platform team's goal is to build all the necessary and advanced tooling to support full-lifecycle operation and management of domestic and overseas data centers, covering data center infrastructure operation and maintenance, server delivery, server operation, asset and resource management, platform tooling, as well as cloud services and server system services.
We are looking for an architect-level contributor to lead the design, development, and adoption of enterprise AI agent capabilities, AI harness platforms, RAG-based knowledge base systems, and emerging AI technologies across the organization. This role requires deep technical architecture expertise, strong hands-on understanding of AI platforms, and product management capabilities to identify business needs, shape platform capabilities, and drive enterprise-wide adoption.
Job Responsibilities
- Possess an in-depth understanding of the history and industry trends in Server Operations and Data Center operations platform development from production point of view. Be familiar with Volcano (Volcengine) and edge server / Data Center requirements and their differences globally. Acquire knowledge of both domestic and non-domestic GPU hardware architectures, firmware baselines, and performance stress testing. Master high-power-density and high-compute-density rack-level solutions, including cold-plate liquid cooling integrated with air cooling, PowerShelf, SideCar, and RDMA copper / optical interconnects such as NVLINK or HCCS. Leverage the AI Factory approach to address challenges at the AI infrastructure layer.
- Define the technical architecture and roadmap for enterprise AI agent platforms, including AI harness, agent orchestration, tool integration, workflow automation, memory, evaluation, observability, and governance capabilities.
- Partner with product, engineering, data, security, and business teams to identify high-value AI use cases and guide them from concept to production.
- Establish architecture principles, design patterns, best practices, and governance standards for enterprise AI agent and RAG-based application development.
- Provide technical leadership on AI reliability, scalability, observability, cost optimization, privacy, security, compliance, and responsible AI practices.
- Apply product management thinking to define platform vision, user journeys, feature priorities, adoption metrics, rollout plans, and feedback loops.
- Influence stakeholders across business and technology teams to align AI platform capabilities with enterprise strategy.
- Mentor engineers and platform teams through architecture reviews, technical deep dives, prototypes, and implementation guidance.
Requirements
Minimum Qualifications
- BS degree in Computer Science, similar technical field of study or equivalent practical experience.
- Experience working with two or more of the following: web application development, Unix/Linux environments, distributed and parallel systems, networking systems, developing large software systems.
- 5+ years software development experience preferably with Golang or Python.
- Experience driving enterprise AI adoption across multiple business units or internal product teams.
- Prior experience combining architect-level technical ownership with product management responsibilities.
- Strong understanding of AI system tradeoffs, including cost, latency, accuracy, reliability, safety, and user experience.
Preferred Qualifications:
- Experience building or architecting enterprise AI agent platforms, AI harness platforms, RAG-based knowledge bases, model gateways, evaluation platforms, or internal AI developer platforms.
- Deep familiarity with modern LLM ecosystems, including agent frameworks, vector databases, embedding models, reranking models, model routing, evaluation tooling, and AI governance.
- Experience with enterprise knowledge management systems, document intelligence, semantic search, taxonomy design, or knowledge quality evaluation.
- Experience with security, compliance, data privacy, access control, auditability, and responsible AI practices in enterprise AI systems.
- Publications, patents, open-source contributions, or recognized technical leadership in AI, platform architecture, or enterprise software are a plus.