Data & AI Solution Architect

Company DescriptionSingdata is specializing in next-generation AI and data solutions for modern enterprises. Powered by proprietary technologies "Single-Engine" and "Generic Incremental Compute," Singdata’s lakehouse product streamlines data platform architectures, delivering exceptional performance and return on investment. By leveraging AI for Data and Data for AI, Singdata transforms data engineering and analytics processes. Its cloud-agnostic and AI-native data infrastructure powers cutting-edge data agents to drive innovation and efficiency across industries.Position Description1. Provide consulting services to key customers on Singdata Lakehouse and AI solutions, driving their Data + AI transformation journey2. Conduct AI Agent use case discovery, lead solution design and architecture, and drive end-to-end project implementation3. Work with local sales team to ensure products meet customers' business and technical requirements4. Build reusable solution assets and reference architectures; deliver training, tech talks, and workshops to customers and partners5. Drive high customer satisfaction in all engagements6. Travel within APAC is expected (30%)Core Requirements1. Bachelor's degree in Computer Science, Engineering, or related field; 5+ years of related work experience2. Solid skills with at least one mainstream big data platform: Snowflake / Databricks / ClickHouse / Hadoop / Spark3. Hands-on coding experience, Al coding experience, especially for mission-critical and low-latency systems4. Experience in architecting and deploying data engineering infrastructure; good understanding of the Big Data industry5. Fluent English and Mandarin required (speaking and reading) for collaboration with China-based teams（Cantonese is required for positions in Hong Kong to ensure effective communication with local clients, colleagues and stakeholders.）6. Strong ownership, self-motivation, and passion for helping customers realize business valueAI/LLM Requirements (Preferred)7. Understanding of LLM fundamentals: model architectures (GPT, LLaMA, Qwen), fine-tuning (LoRA, PEFT), and prompt engineering8. Experience with RAG systems, vector databases (Milvus, Pinecone, pgvector), and embedding models9. Familiarity with AI Agent frameworks: LangChain, LlamaIndex, CrewAI, or similar tools10. Exposure to enterprise AI platforms (Dify, FastGPT) or cloud AI services (AWS Bedrock, Azure OpenAI, GCP Vertex AI) is a plus