At GEICO, the business problem is to transform the claims experience from intake to settlement using Generative AI, making claims faster, more accurate, and more transparent.
Requirements
- 5+ years of experience in applied machine learning or AI product engineering, including 2+ years working with Generative AI or LLMs.
- Deep hands-on expertise in Python, LangChain, LlamaIndex, Hugging Face, or OpenAI/Anthropic APIs.
- Proven ability to design, prototype, and ship AI solutions in real-world domains (e.g., insurance, healthcare, finance, or legal).
- Strong understanding of retrieval-augmented generation (RAG), prompt engineering, text-to-structure extraction, and vector database design.
- Ability to lead through influence, driving vision, technical direction, and experimentation within a small, cross-functional team.
- A track record of turning research ideas into working products with measurable outcomes.
- Experience in building machine learning models.
Responsibilities
- Design and lead applied AI initiatives across the claim lifecycle (e.g., Claim intake, automation, document understanding, adjuster assist, fraud detection, coverage reasoning).
- Prototype and build GenAI-powered tools from RAG pipelines and domain-tuned LLMs to intelligent workflow agents that operate on structured and unstructured claims data.
- Experiment rigorously: define hypotheses, run evaluations, measure impact, and refine models in production.
- Collaborate cross-functionally with product, data, and claims operations to translate pain points into scalable AI solutions.
- Guide a small team of engineers and scientists by influence, providing technical direction and mentorship on AI architecture, experimentation, and deployment.
- Set standards for AI reliability, interpretability, and governance tailored to claims and regulatory constraints.
- Stay on the frontier of AI capabilities continuously evaluating new tools, models, and frameworks to apply responsibly in claims contexts.
Other
- A builder first hands-on with code, experiments, and prototypes.
- A strategic thinker who connects emerging AI capability with tangible business impact.
- A collaborative problem solver who thrives in ambiguous, high-ownership environments.
- Motivated by real-world outcomes, not just model performance.
- Strong communication skills capable of articulating technical insights to business and product stakeholders.
