AI Agent Intern (Semantic Search) at NIO in San Jose-

Summary: NIO is a pioneer and a leading company in the premium smart electric vehicle market. They are seeking an AI Agent Intern to join their AI Platform team, focusing on building intelligent agents that bridge natural language with various data sources. The intern will work on document intelligence, structured data querying, and metadata-aware reasoning to enhance access to the company's internal knowledge and data. Responsibilities: - Join our AI Platform team to build intelligent agents that unlock the full value of our company's internal knowledge and data - You will work at the intersection of large language models, enterprise data systems, and agentic workflow design — creating tools that allow employees and systems to query, interpret, and act on everything from unstructured documents to live operational databases - This internship is hands-on from day one - You will own a real workstream, collaborate with senior engineers and data architects, and ship features that are used in production before the summer ends - Your work will focus on building an AI agent layer that bridges natural language with these data sources - Specifically, you will tackle three interconnected areas: Document intelligence — indexing and retrieving relevant content from design and operations documents using semantic search and RAG pipelines - Structured data querying — enabling the agent to generate, validate, and execute SQL or API queries against both static (data warehouse) and real-time vehicle structured datasets - Metadata-aware reasoning — integrating data catalog metadata so the agent understands schema context, data freshness, ownership, and access policies before surfacing results - Deliverables: Document retrieval agent. A working RAG pipeline over internal design and ops documents with a conversational query interface - Data query module. A text-to-SQL agent component capable of querying both static warehouse tables and real-time data sources - Metadata integration layer. A connector links the agent to the data catalog so it can reason about schema, lineage, and freshness - Evaluation report. A benchmark suite and written analysis covering accuracy, latency, and failure modes of the agent system - End-of-internship demo. A live presentation to engineering and data leadership showcasing the system and key findings Required Qualifications: - Currently study in graduate school of Computer Science, Data Science, Software Engineering, or a closely related technical field - Solid Python programming skills, including familiarity with standard data and ML libraries (pandas, NumPy, PyTorch or similar) - Working knowledge of SQL — ability to write, read, and debug queries against relational databases - Familiarity with large language model APIs (OpenAI, Anthropic, or equivalent) and at least one hands-on project using them - Understanding of basic information retrieval concepts: similarity search, relevance search, and the fundamentals of RAG - Ability to read and understand REST API documentation and integrate third-party services in code - Strong written and verbal communication skills, with the ability to document technical work clearly for both technical and non-technical audiences Preferred Qualifications: - Currently pursuing a PhD Program is preferred - Experience with agentic AI frameworks such as LangChain, LlamaIndex, AutoGen, or comparable libraries. Strong plus - Familiarity with data catalog or metadata management tools (Datahub, Amundsen, Apache Atlas, dbt docs) - Exposure to real-time or streaming data systems such as Apache Kafka, Spark Structured Streaming, or Flink - Experience with cloud data platforms — Redshift, BigQuery or Databricks - Knowledge of enterprise data governance concepts: data lineage, access control, PII classification, and schema versioning - Prior internship or research experience in an AI, data engineering, or ML platform role. Strong plus Required Skills: Python programming, Data science libraries, Machine learning libraries, SQL, Large language model APIs, Information retrieval, REST API integration