Applied AI Engineer Intern

Through our dedicated associates, Conduent delivers mission\-critical services and solutions on behalf of Fortune 100 companies and over 500 governments \- creating exceptional outcomes for our clients and the millions of people who count on them. You have an opportunity to personally thrive, make a difference and be part of a culture where individuality is noticed and valued every day. **Applied AI Engineer Intern – Remote – Conduent** **Position Overview** Through our dedicated associates, Conduent delivers mission\-critical services and solutions on behalf of Fortune 100 companies and over 500 governments – creating exceptional outcomes for our clients and the millions of people who count on them. We are seeking a remote Applied AI Engineer Intern to support AI\-enabled initiatives within Conduent Government Healthcare Solutions, focused on organizing enterprise knowledge, improving information retrieval, and helping teams make better use of existing data and documentation. This internship is remote and is expected to work 20 hours per week. **This posting represents a focused internship opportunity** for a student interested in applying AI engineering, data analysis, automation, and emerging technology concepts to practical business challenges. The intern will gain hands\-on experience supporting applied AI projects that may include reviewing and organizing enterprise content, assisting with data preparation and information\-extraction workflows, evaluating retrieval approaches, supporting AI\-enabled knowledge access, and documenting findings, limitations, and recommended next steps. This internship is part of a structured fall program that includes mentorship, networking opportunities, and exposure to senior leadership. **Key Responsibilities** * Work with a team\-provided, sampled, design\-only corpus to assess document types, metadata, domain concepts, relationships, and quality issues that affect retrieval; scope will be established with the team. * Build preprocessing and LLM\-assisted extraction workflows that transform unstructured design artifacts into candidate structured representations, including concepts, relationships, provenance, and concise summaries, with human validation. * Establish an embeddings/vector\-index retrieval baseline as an experiment on the sampled corpus and compare it with richer approaches; the baseline is not intended to serve as a production tester\-facing tool or test\-authoring pipeline. * Explore knowledge graphs, knowledge maps, and other structured indexes, and use evidence to compare their performance with the vector baseline without assuming that one representation will be superior. * Use sequential LLM\-assisted distillation and assimilation to produce a reusable written knowledge digest, or equivalent, layered on top of the index and/or graph for a representative sample selected with the team. * Demonstrate, with supervisor review, that retrieved and distilled knowledge