Build and maintain vector stores for RAG across MeridianLink products.
Own the feature store for model training and online/offline inference.
Design graph databases modeling relationships among applicants, products, lenders, decisions.
Lead data discovery to surface lending, deposit, and behavior trends.
Engineer AI-ready datasets with quality, documentation, and governance baked in.
Evaluate retrieval and feature quality; define evaluation frameworks and iterate.

🎯 Requirements

4–7 years in data science, ML eng, or applied data roles.
Hands-on vector stores design/operation for RAG or semantic search.
Experience building/operating a feature store (Databricks, Feast, etc.).
Graph data modeling with Neo4j, TigerGraph, or Cosmos Gremlin.
Strong Python (pandas, NumPy, scikit-learn, PySpark) and SQL.
Experience with embedding models and LLM tooling (HuggingFace, OpenAI).

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