Applied AI Engineer Intern

About the role

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.

Responsibilities

• Work with a team-provided, sampled, design-only corpus to assess document types, metadata, domain concepts, relationships, and quality issues that affect retrieval;
• 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;
• 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 is grounded and reusable through identifiers, provenance, and human-checkable excerpts;
• Define and track retrieval and knowledge-quality measures, such as relevance, coverage, precision of retrieved context, redundancy, provenance and groundedness, completeness, consistency, and human validation, including review with QA practitioners;
• Document architecture decisions, experiments, results, limitations, failure modes, and recommended next steps, including how Conduent teams can maintain the Azure-based experiments.

Qualifications

• Currently enrolled in a graduate program, or have equivalent preparation, in Data Science, Computer Science, Natural Language Processing, Information Retrieval, Machine Learning, Artificial Intelligence, Engineering, or a closely related field;
• Coursework, project experience, or demonstrated work involving natural language processing, information extraction, vector search, retrieval-augmented generation as an experimental method, knowledge representation, applied AI for unstructured enterprise text, or experimental design and evaluation;
• Comfort working with messy, unstructured text and conducting quantitative evaluation and human review—not only developing demonstrations;
• Ability and willingness to build and empirically compare multiple representations rather than relying on a single vector index;
• Strong written communication skills and the ability to explain technical work clearly to both technical and non-technical audiences;
• Familiarity with cloud platforms, particularly Microsoft Azure, is helpful; approved project resources will be provided by Conduent;
• Ability to work independently in a remote environment while collaborating effectively, incorporating feedback, and providing regular progress updates.

Project Requirements and Constraints

• Use only the team-provided, sampled, test-design corpus;
• Test execution results, runs, outcomes, test points, result attachments, member or production personal data, and claim or encounter payloads are out of scope;
• Keep all work and project artifacts on Conduent-controlled systems, including the team-provided Azure environment;
• Program materials may not be copied to personal cloud storage, consumer AI tools, university systems, or other unapproved resources;
• Treat all internship work products as Conduent intellectual property;
• Thesis use, course repositories, dual-ownership arrangements, or external disclosure are not authorized without prior Conduent approval;
• Maintain human review throughout the work. Retrieval and assimilation may support people and internal tools, but must not author, generate, or publish test assets or bypass QA processes;
• Limit the internship to research, representation, retrieval, assimilation, prototyping, and evaluation.