Intern AI Engineer, Early-Career – LLM Context & Data Layer (Healthcare)

Overview: We are seeking an Intern-AI Engineer to help design, build, and deploy AI-powered applications that leverage large language models (LLMs) and enterprise data systems. This role focuses on developing practical AI solutions that improve healthcare treatment, payment, and operations through intelligent systems and natural language interfaces.

You will work closely with Data Scientists, Data Engineers, and business stakeholders to build the underlying architecture for AI agents - including context layers, data retrieval systems, and governance guardrails - that operate reliably in regulated, data-intensive healthcare environments. This is an excellent opportunity for an early-career engineer passionate about AI technologies and building real-world applications that scale. *We are currently looking for interns who can start immediately, 12-week internship, and work 40 hours per week.* Responsibilities: Technical Expertise

• SQL fundamentals - comfortable writing queries against structured data, not just describing them
• Exposure to LLM APIs (e.g., OpenAI, Azure OpenAI, Anthropic) and familiarity with one orchestration framework (LangChain, LlamaIndex, or similar)
• Git basics - comfortable with branching and reviewing code as part of a team workflow
• Experience building or designing semantic layers, knowledge graphs, or context retrieval systems (RAG pipelines); understands embedding models, chunking strategies, and retrieval optimization
• Ability to reason about data quality, duplication, and freshness - especially critical for training and maintaining embeddings
• Fetch and ingest data from live external sources (APIs and web sources) into a structured pipeline - handling formats, rate limits, and failures, not just calling a pre-built connector
• Design and build the schema and storage layer your pipeline writes to, in collaboration with data engineering, comfortable with vector storage or semantic indexing patterns