Intern AI Engineer, Early-Career – LLM Context & Data Layer (Healthcare)
Intern builds and tests AI components for healthcare informatics, using LLM APIs, orchestration frameworks, and data pipelines to improve treatment, payment, and operations.
- Compensation
- $32–$40 / hr
- Location
- Remote, US
- Work setup
- Remote
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Currently pursuing or recently completed an advanced degree in healthcare, technology, or a related field (e.g., Biomedical Informatics, Computer Science) with preference of a PhD.
- Demonstrated interest or experience in AI, healthcare technology, or informatics research.
- Strong foundational knowledge in generative AI model development, architectures, and vector databases.
- Hands-on experience with working with Machine Learning and Deep Learning models. Experience with LLM/RAG models and LLM fine-tuning is a plus.
- Hands-on experience working with cloud services (AWS/Azure), large data sets, and building data pipelines for ML solutions. Experience with vector embeddings and databases is a plus.
- Ability to work collaboratively and communicate effectively with cross-functional teams.
Full description
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.
- Build and test AI components - embeddings, retrieval logic, prompts - using established patterns and tools; iterate on context relevance and accuracy.
Communication & Collaboration
- Communicates technology-related updates and requirements to other departments and contributes to presentations for senior management.
- Collaborates with research and development teams, product management, and strategic analysts to support ongoing projects.
Leadership & Management
- Partners with Business Units to provide reliable intelligence, validated technology options, and insights on enterprise and industry trends.
- Supports technology project teams by coordinating specific tasks, assisting with day-to-day operations, and contributing to the successful delivery of solutions.