Principal Software Engineer; Python
Listed on 2026-06-19
-
Software Development
AI Engineer (Applied/Software), Backend Developer, Python
At Cotality, we are driven by a single mission—to make the property industry faster, smarter, and more people‑centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.
Job DescriptionThe hybrid‑remote Principal Software Development Engineer leads the design, development, and testing of complex software systems and applications. This senior‑level position is responsible for creating scalable, high‑quality software solutions while providing technical leadership, driving engineering excellence, and providing mentorship to engineering teams.
The MissionYou will take the helm of our existing Semantic Layer Web Service and lead its evolution into a high‑scale, automated intelligence platform. Your mission is to break the current manual bottlenecks—moving the service from 130 manually curated attributes to a system capable of managing thousands. You will bridge the gap between "boutique" internal tools and a fully automated, GraphRAG‑enabled enterprise ecosystem.
Job Responsibilities- 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. - Implement Advanced Reasoning:
Design and deploy Hybrid Graph/Vector search architectures (GraphRAG) to enable complex global reasoning across disparate enterprise data assets. - Develop High‑Performance AI Services:
Utilize Git or similar, Unix command line, Python, FastAPI, FastMCP, and ADK to build and enhance robust backend services that bridge core enterprise data with Agentic AI. - Model Optimization (LLM & SLM):
Partner with the Data Science team to architect intelligent routing logic that leverages Small Language Models (SLMs) for high‑speed classification/extraction and Large Language Models (LLMs) for complex synthesis. - Automate Metadata Ingestion:
Collaborate with the Knowledge Engineering team to build automated workflows for Ontology Management and Entity Linking, eliminating single‑resource bottlenecks and manual curation constraints. - Establish Systematic Evaluation:
Collaborate with Knowledge Engineering and Data Science teams to replace "vibe checks" with rigorous automated testing frameworks, utilizing metrics like Faithfulness, Answer Relevance, and Context Precision (RAGAS) to ensure quality during rapid scaling. - Conduct Expert Code Reviews:
Ensure high code quality, adherence to AI engineering standards, and architectural integrity across the Python/AI stack. - Drive Test‑Driven Development (TDD):
Implement rigorous TDD practices, writing comprehensive unit and integration tests to ensure the reliability of non‑deterministic semantic pipelines. - Apply Advanced Algorithms:
Utilize specialized algorithms for Entity Disambiguation and Ontology Evolution to solve complex knowledge‑graph scaling and schema‑drift challenges. - Optimize Performance and Scalability:
Independently plan and execute system optimizations to ensure the Semantic Layer remains performant under enterprise‑scale loads and high‑dimensional search requirements. - Troubleshoot Complex AI Issues:
Resolve deep technical challenges related to model hallucinations, "Lost in the Middle" context window phenomena, and retrieval latency. - Stay Updated on Emerging AI Trends:
Monitor and provide recommendations on the rapidly evolving AI landscape, including SLM success areas, prompt engineering, and synthetic data generation. - Mentor Senior & Junior Engineers:
Act as a technical mentor and coach, sharing expertise in Python, Semantic AI,…
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).