As an EO Software Architect, you will lead the end-to-end technical architecture design of a large-scale spatiotemporal data intelligence platform for overseas markets, covering the full-stack architecture from data ingestion, storage and computing to intelligent analytics and application layers. You will deeply integrate AI with spatiotemporal intelligence, leveraging AI tools to accelerate architecture validation, prototype iteration, and cross-team collaboration. You will take full ownership of the product’s technical innovation, scalability, performance, security compliance, and commercial success.

1. Overall Architecture Design & Technology Planning
Lead end-to-end architecture design across multiple core product lines based on product strategy and roadmap planning. Define 1–3 year technology roadmaps, including layered architecture, module boundaries, technology selection, and evolution strategies.
2. Core Data Platform Architecture
Design storage, computing, indexing, and retrieval solutions for PB-scale multi-source heterogeneous data, including imagery, vector data, elevation data, and trajectory data. Lead the implementation of core capabilities such as unified spatiotemporal storage, visualization systems, knowledge graph construction, and multi-source heterogeneous data integration. Technology stacks may include distributed storage, columnar/time-series/graph databases, and search engines.
3. Multi-Source Data Fusion Architecture
Design architecture for multi-source heterogeneous data ingestion, parsing, deduplication, source verification, quality grading, and fusion analysis pipelines. Build core analytical engines for event generation, spatiotemporal attribute association, relationship mining, and event chain tracing.
4. AI & Algorithm Platform Architecture
Design AI-driven spatiotemporal analytics platform architecture covering data labeling, model training, inference services, object detection, change detection, image analysis, time-series analysis, and trajectory analytics. Drive the deep integration of multimodal AI, foundation models, and spatiotemporal reasoning capabilities into business systems.
5. Visualization & Situation Awareness Engine Architecture
Lead the architecture design of 2D/3D visualization engines, unified dashboards, intelligent situation awareness systems, and simulation engines. Support high-concurrency rendering, large-scale entity visualization, real-time event monitoring, and decision simulation capabilities.
6. AI Builder · High-Efficiency Architecture Delivery
Leverage AI tools such as Claude Code, Cursor, and v0 to accelerate architecture validation (PoC), technical research, ADR documentation, prototyping, and code reviews. Establish AI-driven engineering best practices within the team and significantly improve the speed from architecture concept to implementation. Promote AI-native architecture thinking by designing AI as a first-class system capability.
7. Engineering Collaboration & Team Enablement
Collaborate closely with domestic R&D teams to guide technical direction and ensure code quality across key modules. Lead architecture reviews, technical decision-making, and code reviews for core systems. Mentor senior engineers and produce high-quality architecture documentation and technical standards.

1. Education & Background
Bachelor’s degree or above, preferably in Computer Science, Software Engineering, GIS, Remote Sensing, Artificial Intelligence, or related fields. Strong English communication skills, both written and verbal, with the ability to collaborate effectively across cultures.
2. Architecture Experience
3+ years of experience in software architect roles and 5+ years of backend/platform engineering experience. Proven track record leading end-to-end architecture design and implementation for large-scale distributed systems, GIS platforms, or AI platforms. Strong system abstraction and layered architecture design capabilities.
3. Data Technology Stack
Familiar with multiple core technologies such as PostgreSQL/PostGIS, GeoServer, Cesium/Mapbox/OSGEarth, Elasticsearch, time-series databases (InfluxDB/TDengine), and graph databases (Neo4j/NebulaGraph). Understanding of structured, unstructured, and time-series spatiotemporal data processing pipelines.
4. AI & Data Platform Expertise
Familiar with MLOps and model training/inference architectures such as Kubeflow, Triton, and vLLM. Hands-on experience with CV, multimodal AI, and LLM integration projects. Understanding of engineering challenges related to spatiotemporal reasoning, knowledge graphs, and Agent-based systems.
5. Cloud-Native & High-Concurrency Systems
Strong expertise in Kubernetes, microservices, message queues (Kafka/Pulsar), stream processing (Flink/Spark Streaming), and containerized cloud-native architectures. Experience designing large-scale data pipelines and high-concurrency systems.
6. AI Tool Proficiency (Key Requirement)
Heavy user of AI engineering tools such as Claude Code, Cursor, Claude/ChatGPT, and v0 for architecture validation, PoC development, documentation, and code reviews. Able to demonstrate practical AI-driven engineering cases and measurable efficiency improvements. Passionate about driving AI engineering best practices across teams.
7. Industry Experience (Preferred)
Experience with government-facing or large-scale enterprise projects is preferred. Familiarity with overseas IT infrastructure environments, data compliance requirements, and procurement practices is a plus.
8. Core Competencies
Able to adapt to frequent business travel, highly goal-oriented, resilient under pressure, and technically driven. Excellent customer communication and cross-cultural collaboration skills are essential.
9. Compliance & Confidentiality
Strong awareness of confidentiality and compliance requirements, with no history of legal, disciplinary, or confidentiality violations. Able to support frequent overseas customer engagement and collaborate across departments and regions on complex projects.
10. Communication & Collaboration Skills
Excellent communication and interpersonal skills, with the ability to effectively engage with customers, collaborate across cross-functional and international teams, and drive alignment on complex technical and business initiatives.