AI及云端训练前瞻科学家_XC
Core
Define end-to-cloud technical direction for AI and data/cloud computing, bridging next-gen algorithms (LLMs, E2E Driving) with data infrastructure for data closed-loops.
Role type
Principal AI & Cloud Architect (Strategy & Infrastructure)
Builds
End-to-cloud data infrastructure, cloud-native simulation platforms, and hybrid compute strategies for autonomous driving.
Domain
Autonomous Driving / Cloud Computing / Data Infrastructure
Deliverable
production ML models | infrastructure
Required skills
Cloud-native architecture (Kubernetes, Docker, Microservices), Data infrastructure design (Data Lakes, Vector Databases, Data Pipelines), AI Engineering (MLOps, Model Deployment), Hybrid compute strategy design, Cloud cost optimization (FinOps, TCO analysis), Large-scale simulation platform design.
Preferred skills
Experience with Sovereign Cloud compliance, RAG systems, Shadow Mode data processing, Vehicle-Cloud collaborative computing protocols.
Technologies
Kubernetes, Docker, AWS, Azure, Alibaba Cloud, Snowflake, Databricks, Milvus, Pinecone, Kafka, Airflow, Kubeflow, MLflow, ISO 8800.
Responsibilities
Track global cloud-native automotive architecture trends and sovereign cloud compliance; Design automated data processing architectures from collection to OTA updates; Lead design of large-scale cloud simulation platforms for L3/L4 validation; Conduct deep technical analysis on cloud ROI, FinOps, and Make vs Buy decisions; Guide innovation squads in developing cloud-native PoCs for RAG and Shadow Mode.
Seniority
Principal, Strategy & Mentorship