Temporary Intern - Agentic AI & Neural-Symbolic… at Cotiviti · Remote

Summary: Cotiviti is seeking two highly motivated undergraduate interns to work on next-generation Agentic AI platforms and neural-symbolic systems in the healthcare domain. Interns will collaborate closely with engineers, data scientists, and product teams to support complex use cases involving reasoning, compliance, and explainability. Responsibilities: - Agentic Framework Evaluation - Design and run evaluations for LLM-based agents (quality, reliability, safety) - Analyze agent behavior and identify failure modes - Propose improvements based on empirical results - Agentic Platform Development - Build and enhance components such as tools, skills, memory systems, and MCP integrations - Improve orchestration, observability, and developer experience - Prototype new capabilities for real-world workflows - Neural-Symbolic AI (Healthcare Focus) - Explore approaches combining LLMs with rules, knowledge graphs, or symbolic reasoning - Work on use cases requiring auditability and explainability (e.g., claims, policy logic) - Help bridge probabilistic AI with deterministic systems - Forward-Deployed Engineering Experiments - Partner closely with internal stakeholders or pilot users - Rapidly prototype and iterate on solutions in real-world contexts - Help define patterns for scaling adoption of agentic systems Required Qualifications: - Currently pursuing a Bachelor's degree (Freshman–Senior) in related field - Hands-on experience with Python or similar programming languages - Familiarity with LLMs / GenAI (e.g., building projects with OpenAI, Claude, Gemini, or open-source models) - Strong curiosity and willingness to learn quickly in ambiguous environments - Ability to break down problems and iterate through experimentation Required Skills: Python, Programming languages, Large Language Models (LLMs), Generative AI, Neural-symbolic AI, Knowledge graphs, Symbolic reasoning, Auditability, Explainability, Probabilistic AI, Deterministic systems, Curiosity, Willingness to learn