Artificial Intelligence Intern

Job descriptionAI InternLocation: In Person (Tempe, AZ)Duration: 3-month Internship (Full time offer based on performance)Compensation: $17–$20 per hourStart Date: Mid May 2026About the RoleWe are looking for a motivated AI Intern who is passionate about large language models (LLMs), machine learning, and applied AI systems. This internship is focused on building real-world AI systems, not just experimenting with models.You will work on projects involving open-source LLMs, retrieval-augmented generation (RAG) pipelines, vector databases, and AI-powered document processing systems. The goal is to build scalable AI workflows that solve practical problems such as knowledge retrieval, document analysis, and AI-assisted automation.This role is ideal for students or early-career engineers who want hands-on experience deploying AI systems used in production-like environments.ResponsibilitiesAI & Machine Learning DevelopmentBuild and experiment with open-source LLM and SLM pipelines.Design and implement Retrieval Augmented Generation (RAG) systems.Develop AI pipelines capable of processing documents, PDFs, and structured data.Work with embedding models and vector search systems.Implement prompt engineering, model evaluation, and response optimization.Assist with fine-tuning or adapting open-source models when necessary.Data Processing & AI PipelinesBuild ingestion pipelines for PDFs, documents, and datasets.Implement document chunking, embedding generation, and indexing strategies.Work with vector databases to support semantic search and retrieval.Optimize pipelines for latency, scalability, and cost efficiency.Research & ExperimentationEvaluate different open-source models and architectures.Compare embedding models and retrieval methods.Test improvements in RAG performance and hallucination reduction.Explore emerging techniques in Vision Language Models (VLMs).CollaborationWork with engineers to integrate AI components into applications.Document experiments and technical findings.Participate in weekly discussions on AI architecture decisions and improvements.Required Skills & KnowledgeCore AI KnowledgeStrong understanding of:Machine LearningNeural NetworksDeep Learning fundamentalsFamiliarity with:Large Language Models (LLMs)Small Language Models (SLMs)ProgrammingStrong proficiency in PythonExperience with AI/ML frameworks such as:PyTorchTensorFlowHugging Face TransformersAI Application FrameworksExperience with:LangChain or similar orchestration frameworksPrompt engineering and AI workflow buildingVector DatabasesConceptual and practical understanding of any vector databases such as:PineconeChromaDBMilvusQdrantFAISSWeaviateUnderstanding of:embeddingssimilarity searchindexing strategiesmetadata filteringRAG SystemsAbility to design or understand:Retrieval pipelinesDocument chunking strategiesEmbedding pipelinesHybrid searchContext window optimizationRAG evaluation methodsData ProcessingExperience with:PDF extractionDocument parsing pipelinesData preprocessingBonus KnowledgeVision Language Models (VLMs)Multimodal AI systemsDistributed AI inferenceGPU inference optimizationPreferred Project ExperienceCandidates should have completed at least 1–3 hands-on AI projects, such as:Example Project 1 – RAG Knowledge AssistantBuilt a chatbot that answers questions from internal documentation.Implemented document ingestion, chunking, embedding generation, and vector search.Used LangChain + vector database + open-source LLM.Example Project 2 – Document AI SystemCreated a system that extracts structured information from PDFs.Built pipelines for PDF parsing → embeddings → AI summarization.Example Project 3 – AI Research ExperimentCompared multiple embedding models and evaluated search accuracy.Benchmarked RAG response quality and hallucination rates.Example Project 4 – LLM ApplicationBuilt a real-world tool using open-source models (e.g., summarizer, Q&A system, coding assistant).Real-World Experience (Nice to Have)Candidates may also have experience such as:Contributing to open-source AI projectsParticipating in AI hackathonsResearch experience in machine learning or NLPBuilding production-style AI APIsDeploying models using Docker or cloud platformsWorking with LLM inference servers (vLLM, TGI, Ollama, etc.)Tools & Technologies (Exposure Preferred)AI / MLPyTorchHugging FaceTransformersSentence TransformersLLM ToolsLangChainLlamaIndexOpen-source LLMs (LLaMA, Mistral, etc.)Vector DatabasesPineconeChromaDBMilvusQdrantFAISSWeaviateData ToolsPandasNumPyDeploymentDockerREST APIs / FastAPIBasic cloud exposure (AWS/GCP/Azure)What You Will GainHands-on experience building real AI systemsExposure to modern LLM architecture and AI infrastructureExperience with RAG pipelines and vector databasesMentorship from engineers working in applied AIOpportunity to contribute to real production-style AI toolsCandidate ProfileWe are looking for someone who:Is curious and loves experimenting with AI systemsEnjoys solving practical engineering problemsCan quickly learn new frameworks and modelsIs comfortable reading research papers and technical documentationHas strong problem-solving and debugging skillsApplication RequirementsPlease include:ResumeGitHub profile (required)Links to AI/ML projectsBrief description of a RAG or LLM system you have builtWork Location: In person