AI/ML Engineer
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Develop Semantic Mapping Models using LLMs (Large Language Models) to match field descriptions even when labels differ (e.g., mapping "Last Traded Price" to PX_LAST).
Implement Fuzzy Matching Algorithms (Levenshtein, Jaro-Winkler, Cosine Similarity) to handle near-matches in text strings.
Build Vector Embeddings of the Vendor Data Dictionary to enable high-speed semantic search.
Design a "Confidence Score" framework to flag low-certainty matches for human review.
AI/ML Skills:
LLMs & NLP: Experience with OpenAI API, LangChain, or HuggingFace Transformers for text-to-field understanding.
Vector Databases: Proficiency in Pinecone, Milvus, or Weaviate for managing high-dimensional data embeddings.
Automation: Building "Human-in-the-Loop" (HITL) workflows to retrain models based on SME feedback.
Tools: Python (PyTorch/TensorFlow), Scikit-learn, BERT, Pandas.