Fabián del Valle

AI Researcher

New York City, New York, United States2 yrs 6 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Architected AI systems at scale for J.P. Morgan Chase.
Achieved 99% FLOP reduction in neural network pruning.
Led teams to deliver impactful ML solutions.

Stackforce AI infers this person is a Fintech AI Engineer specializing in scalable machine learning systems.

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Skills

Core Skills

Machine LearningAi Engineering

Other Skills

AI AgentsLarge Language Models (LLM)Generative AINatural Language Processing (NLP)FastAPIMicroservicesLocality-Sensitive HashingAnomaly DetectionSurrogate Polynomial RegressionFine TuningRetrieval-Augmented Generation (RAG)MLOpsTensorFlowDockerHigh Performance Computing (HPC)

About

Currently at J.P. Morgan Chase, I architect multi-agent AI orchestrators, production anomaly detection platforms at 100K+ portfolio scale, and LLM-powered pipelines that cut analyst workflows from hours to minutes. I've led teams, shipped systems, and delivered ML that people actually depend on. My research background is in locality-sensitive hashing — I've applied LSH to neural network pruning (99% FLOP reduction, 2.4x faster training) and scalable probabilistic clustering, work I did during my M.S. at NYU on a full-tuition scholarship. I think algorithmically, not just empirically. Before grad school I interned at AWS, Verizon, and Synopsys. ML Engineering, Applied Science, and AI/LLM Engineering.

Experience

2 yrs 6 mos

Total Experience

1 yr 3 mos

Average Tenure

1 yr 3 mos

Current Experience

Jpmorganchase

Senior Associate AI Engineer

Mar 2025 – Present · 1 yr 3 mos · New York City Metropolitan Area · On-site

Building production AI systems at one of the world's largest financial institutions.
Designed and shipped a multi-agent AI orchestrator using Tool Registry, Agent Factory, and A2A messaging to coordinate distributed workflows across 10+ structured data sources
Architected a production anomaly detection platform monitoring 100K+ portfolios — reduced analyst review scope by 98% using LSH-based detection, cutting investigation time from 2 hours to 30 minutes
Exposed agentic workflows via FastAPI microservices, reducing data extraction turnaround from hours to minutes
Built interpretable ML models (surrogate polynomial regression) to deliver feature-level attribution reports to 200+ stakeholders
Led ML integration into data pipelines, reducing downstream data defects by 80% and eliminating 10 manual validation roles.

AI AgentsLarge Language Models (LLM)Generative AINatural Language Processing (NLP)Machine LearningAI Engineering

Synopsys inc

2 roles

AI Engineer Intern

May 2024 – Aug 2024 · 3 mos · Austin, TX · Hybrid

AI Engineer Intern

Jun 2023 – Oct 2023 · 4 mos · Austin, Texas, United States · Remote

Duartepino

Junior Data Scientist

Mar 2022 – Jun 2023 · 1 yr 3 mos · San Juan, Puerto Rico · Remote

Amazon web services

Big Data Cloud Intern

May 2021 – Aug 2021 · 3 mos · Dallas, Texas, Estados Unidos

Verizon

AI Engineer Intern

Jun 2020 – Aug 2020 · 2 mos · Basking Ridge, New Jersey, United States

Predictive Analytics Team Lead.
Extracted Live Chat transcripts from the database. Applied Data Engineering, transformed and cleaned the data.
Mined text in the conversations. Preprocessed the text, saved it into a new data frame with the relevant attributes to keep.
Modeled the topics with a Latent Discriminant Analysis. Assigned each conversation a topic with a new column in the data frame.
Implemented classification algorithms such as Random Forest and Stochastic Gradient Descent to predict the problem in the conversation.