PROGRAM | LLM Systems | 2025 -> now
Career Survey Intelligence & Compensation Consistency Engine
LLM-augmented analytics + optimization for high-stakes workflows.
PythonLLM pipelinesOptimizationHybrid rules+MLEvaluation/monitoring
- Parse and normalize free-text roles/narratives into structured signals.
- Detect compensation and seniority inconsistencies with explainable rationales.
- Hybrid design: quantitative models + rules + LLM output grounding.
Context
- In high-stakes domains, accuracy and auditability beat cleverness.
- LLMs are useful as a component, not as the system.
What we built
- Structured extraction from free-text survey content and job descriptions.
- Consistency detection across multi-regional datasets (leveling, role definitions, compensation).
- Human-in-the-loop review: rationales and traceable evidence for decisions.
Engineering choices
- Evaluation discipline: prompt versioning, test sets, regression checks.
- Guardrails: rules + domain constraints to ground LLM outputs.
- Design for explainability and operational monitoring.
RELATED