Job Description

1. AI Strategy: Scouting and incubating next-gen algorithms (LLMs, E2E Driving).
2. 2. Cloud Architecture: Defining the data infrastructure that powers the "Data Closed-Loop"—from vehicle data ingestion and auto-labeling pipelines to cloud-based simulation and model training.
   
   Key Responsibilities:
   
   • Strategic Scouting (AI & Cloud Trends):
   
   o Monitor global trends in Cloud-Native Automotive Architectures, focusing on Hybrid Cloud solutions (Edge-to-Cloud synergy) and Sovereign Cloud compliance in China.
   
   o Scout emerging technologies in Cloud Simulation (e.g., World Models) and Data Management (Vector Databases for RAG, Synthetic Data Generation).
   
   o Build the "AI & Cloud Tech Landscape Map," identifying opportunities to optimize computing costs while maximizing intelligence.
   
   • Cloud & Data Architecture Design (The Backbone):
   
   o Data Closed-Loop: Design the high-level architecture for automated data pipelines: Ingestion → Cleaning → Auto-Labeling (via Foundation Models) → Training → OTA.
   
   o Hybrid Compute Strategy: Define what workloads run on the car (Edge NPU) vs. what workloads offload to the cloud (e.g., Shadow Mode data filtering logic vs. heavy model training).
   
   o Simulation Infrastructure: Lead the technical definition for cloud-based large-scale simulation platforms needed for validating L3/L4 algorithms (ISO 8800 compliance).
   
   • Deep-Dive Analysis (Feasibility & FinOps):
   
   o Author "Deep-Dive Reports" on Cloud ROI & FinOps: Analyze the cost implications of scaling large model training and storage. Provide "Make vs. Buy" recommendations for cloud services (e.g., AWS vs. Azure vs. Private Cloud).
   
   o Evaluate the feasibility of Vehicle-Cloud Collaborative Computing (e.g., RCP - Remote Control Protocol latency analysis).
   
   • Incubation Leadership:
   
   o Guide the Innovation Squad to build Cloud-Native PoCs (e.g., a RAG-based Knowledge Base hosted on cloud with vehicle connectivity, or a Shadow Mode data trigger mechanism).
   
   o Ensure all cloud PoCs adhere to Cybersecurity and Data Privacy regulations.
   
   Qualifications:

Qualifications

Education: Master’s or PhD in Computer Science, Cloud Computing, AI, or related fields.

Experience:

o 8+ years in Software Engineering, with at least 3+ years focused on Cloud Architecture or Data Infrastructure.

o Proven experience in the Automotive, IoT, or Tech industry handling large-scale device data.

• Technical Depth (Cloud & AI Hybrid):

o Cloud Native: Expert knowledge of Kubernetes (K8s), Docker, and Microservices architecture. Familiarity with major public clouds (AWS/Azure/AliCloud).

o Data Infrastructure: Experience with Data Lakes (e.g., Snowflake, Databricks), Vector Databases (e.g., Milvus, Pinecone for LLMs), and Data Pipelines (Kafka, Airflow).

o AI Integration: Understanding of MLOps pipelines (e.g., Kubeflow, MLflow) and how to serve models in the cloud.

• Business Acumen: Ability to calculate Cloud TCO (Total Cost of Ownership) and optimize resource usage (Spot instances, GPU utilization).

• Language: Fluent in English and Mandarin.