AI Engineer Intern - EDS

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal. Job Description

The AI Engineer Intern will assist in designing, prototyping, and integrating Artificial Intelligence solutions across internal enterprise systems and customer-facing platforms. Moving beyond theoretical ML coursework, this role focuses on the practical mechanics of modern AI engineering: working with LLM orchestration frameworks, retrieval systems (RAG), vector storage, and data preparation pipelines under the direct mentorship of senior AI engineers DUTIES AND RESPONSIBILITIES: GenAI & Agent Pipeline Assistance: Support senior engineers in prototyping and testing retrieval-augmented generation (RAG) pipelines, semantic search modules, and basic agentic workflows using frameworks like LangChain or LlamaIndex. Data Ingestion & Semantic Prep: Clean, transform, and index structured and unstructured datasets for embedding generation, vector database storage, and semantic layer integration.

Testing & Model Evaluation: Conduct empirical evaluations of LLM outputs—measuring context relevancy, ground-truth alignment, and mitigation of hallucinations across model revisions. Integration & Reusable Code: Write clean, modular Python code and REST API wrappers to package AI features into reusable SDKs or microservices. Documentation & Version Control: Maintain code hygiene using Git/GitOps best practices and author clear technical documentation for architecture, API endpoints, and setup guides.

Deliverable/s: Completion of a working, end-to-end module (e.g., a functional agent tool or optimized RAG index) presented to the CDA leadership team at the end of Month 4. KPIs: Technical Velocity & Autonomy: Speed at which the intern transitions from requiring step-by-step guidance to independently taking on developer tickets and debugging pipelines. Code & Engineering Quality: Consistency in Git workflows, code readability, unit testing, and adhering to enterprise engineering standards.

Execution on AI Evaluation: Ability to quantitatively measure model performance, prompt reliability, and output accuracy on assigned tasks. SKILLS: Programming: Strong foundation in Python (OOP, async, data structures) and basic SQL AI / GenAI Frameworks: Academic exposure to ML concepts, basic prompt engineering, and API usage (e.g., OpenAI, Hugging Face).

Data & Vector Prep: Experience with pandas, numpy, and parsing unstructured documents (PDFs, JSON, text) Software Tools: Familiarity with Git, Linux command line, and REST API concepts (FastAPI/Flask). Soft Skills & Aptitude: Rapid learning capability, structured problem-solving, curiosity, and attention to detail COMPETENCIES: Core: Problem-Solving: Strong analytical and problem-solving skills to identify and resolve issues. Ability to analyze complex problems, break them into smaller components and devise effective solutions using AI techniques.