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Kenil Tanna
Staff-level Machine Learning Engineer specializing in LLMs and MLOps for Financial Services
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About
Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).
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Key Strengths
- Led production-grade RAG system for financial document retrieval and contextual summarization
- Strong evaluation rigor: retrieval metrics + generation metrics + human-in-the-loop scoring + end-user A/B testing
- Entity resolution across disparate financial sources using hybrid rules + fuzzy matching + Sentence-BERT semantic similarity
- Active learning workflow to scale expert review while maintaining high precision on ambiguous matches
- End-to-end MLOps/productionization on AWS with Docker/Kubernetes, CI/CD, and monitoring (MLflow/Prometheus/Datadog)
- Cross-functional collaboration with domain experts to define relevance criteria and validate business impact (risk assessment/report generation)
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