# Principal Software Engineer (Python) at Cotality (fka CoreLogic)

- Company: Cotality (fka CoreLogic)
- What the company does: Cotality | See the property ecosystem from every angle to reimagine property data as insights that illuminate what’s next. It’s intelligence beyond bounds. Backed by Insight.
- Company website: https://www.corelogic.com/
- Type: Startups
- Level: Principal and up
- Location: 6 Locations
- Work setup: On-site
- Posted: 2026-08-25
- Apply by: 2026-10-09
- Apply: https://cotality.wd115.myworkdayjobs.com/en-US/Global/job/Irvine-CA/Principal-Software-Engineer--Python-_REQ18824-1
- Page: https://www.1752.vc/careers/jobs/cotality-fka-corelogic-principal-software-engineer-python/

## About the role

Architect and Evolve the Semantic Layer: Lead the redesign and scaling of the existing Semantic Layer Web Service, transitioning it from a manual 130-attribute baseline to an automated, high-availability enterprise intelligence platform. Take the Team Anchor Role: Serve as the primary technical lead for the delivery team, owning the architectural vision for Knowledge Representation and Semantic Search while ensuring "fiduciary-grade" performance as the system scales to 1,000+ attributes.

## What they're looking for

- Education & Experience: Master’s or Bachelor’s degree in Computer Science, AI, or a related field, with 10+ years of professional software engineering experience
- Expert Python/FastAPI Developer: Proven experience in designing, scaling, and maintaining existing production web services in an asynchronous Python environment
- Semantic & Graph Expertise: Deep understanding of Knowledge Representation, Ontologies, and Graph Reasoning , experience taking "boutique" internal tools and turning them into high-availability platforms
- Advanced RAG Knowledge: Professional experience with Vector Databases, Hybrid Search, and advanced retrieval strategies (e.g., Query Expansion, HyDE, and Reranking)
- AI Orchestration Mastery: Advanced proficiency in ADK or similar model abstraction tools, with a strong grasp of the operational trade-offs between LLMs and SLMs
- Evaluation Rigor: Familiarity with AI evaluation frameworks ( RAGAS, DeepEval ) and the use of LLMs to generate synthetic testing suites for benchmarking