243K–345K USD / year
Staff Applied AI Engineer - Kalepa for Underwriting
AIRemote — US, Canada
Published on 2026-11-10
These details were extracted automatically from the original listing. They may not be complete or fully up to date — it's worth checking the original job post.
About this role
Set the technical direction for AI systems that turn unstructured insurance data into structured, explainable facts. Lead production-scale extraction, classification, entity matching, and model evaluation, tying system performance to customer outcomes.
About the company
Kalepa builds AI systems for insurers that help automate underwriting, risk analysis, and related insurance workflows. It serves carriers, MGAs, and brokers looking to speed up submission processing and decision-making.
The team
Risk representation and intelligent model-driven insights team, responsible for the proprietary risk representation layer, winnability model, and future models.
Stack
PythonAWSAWS LambdaAmazon EC2LLMsOCRNLP
What you'll do
- Lead technical and project delivery for document extraction, classification, entity matching, and evaluation systems.
- Set architecture and operating standards for LLM systems, balancing accuracy, cost, latency, reliability, and explainability.
- Own benchmark datasets, regression suites, validation strategies, and model release standards.
- Set model and vendor strategy, including decisions about adoption, fine-tuning, and pipeline replacement.
- Partner with product and business leaders to connect model outputs to customer value.
- Manage technical debt, lead code reviews, and promote engineering and deployment best practices.
- Represent Kalepa internally and externally through recruiting and training.
What we're looking for
- 8+ years of engineering experience, including 5+ years operating ML or LLM systems in production.
- Technical lead experience delivering a major applied AI initiative or product.
- Deep expertise in LLM systems, including structured extraction, classification, retrieval, embeddings, evaluation, and production-scale reliability.
- Strong applied ML, NLP, statistics, Python, and data/ML library skills.
- Experience with AWS, serverless technologies, asynchronous workflows, and cloud environments.
- Strong ownership, communication, and ability to drive initiatives with minimal oversight.
Nice to have
- Experience with document AI at scale, including OCR, layout understanding, and multi-page extraction.
- Experience fine-tuning or distilling models for production.
- Experience building evaluation platforms or ML infrastructure used by other teams.
- Familiarity with insurance, underwriting, ACORD forms, or loss runs.
Benefits
- Significant equity options package
- 20 days of PTO per year
- Global team offsites
- Healthy living and gym stipend
- Mobile phone bill stipend
- Continuing education credits
- For US employees: fully covered medical, vision, and dental insurance for individuals and families
- For US employees: 401(k) plan with employer contribution