Open to opportunities

I'm Jerry Adams Franklin.

Senior AI Engineer @ Digital Currency Group · Deep Learning Engineer @ Intel · Ex Data Scientist @ Nokia

Building production-grade intelligent systems, from fine-tuning LLMs and orchestrating AI agents to shipping decentralized ML at scale.

Get in Touch GitHub LinkedIn
$450K+AI-informed investment outcomes led through VCScout-AI at DCG
EthCCInvited talk in Brussels on decentralized AI and federated learning on the FLock network
NEARCONLightning talk on training 7B LLMs on edge devices (NEAR Foundation, San Francisco)
IntelAIA Division Achievement Award for TensorFlow on Windows
Featured at EthCC NEARCON FLock VIT-AP
Experience at
Digital Currency Group, Intel, Nokia, Foundry Digital, OneBill
Jerry Adams Franklin

// About

Building AI that decides, not just predicts.

I'm a Senior AI Engineer at Digital Currency Group, where I architect intelligent, decentralized AI systems that power real-world decisions. I specialize in production-ready AI agents with models like Qwen, LLaMA, Gemma, and Phi, orchestrated with LangChain, LangGraph, and Weaviate vector search, plus RAG, evaluation, and serving patterns used in production.

One of my proudest projects is VCScout-AI, an end-to-end autonomous agent that analyzed 300+ startup deals and directly drove $450K+ in AI-informed investments. My work spans federated learning, blockchain-integrated ML, and fine-tuning LLMs for high-stakes domains like financial trading and smart contract security.

I speak at conferences such as EthCC and NEARCON, guest-lecture at universities (including VIT-AP on production AI and LLM careers), and mentor at nonprofit hackathons like Opportunity Hack with Women in Computer Science at ASU, helping teams ship AI for real nonprofits in 24 hours.

Before DCG, I led TensorFlow CPU performance optimization at Intel, improving core operations significantly and earning the Division Achievement Award. I hold a Master's in Data Science from Northeastern University.

Welcome to my world. Grab a paddle; the AI waters are deep, and we're going swimming.

5+
Years Experience
$450K+
AI-driven Investments
7x
Top FLock tasks
Top 1
FLock AI Arena

// Skills & Expertise

What I work with

Deep expertise across the ML lifecycle, from research and training to production deployment and monitoring.

My knowledge level in machine learning

Languages

Python90%
R80%
C/C++70%
SQL85%

Domain skills

Generative AI90%
Deep Learning90%
Computer Vision75%
NLP80%
🧠

LLMs & Generative AI

QwenLLaMAGemmaPhiFine-tuningLoRAQLoRAPEFTRLHFvLLM
🔗

Agentic AI & RAG

LangChainLangGraphWeaviateVector DBPydanticAITool UseReAct
⚡

ML Infrastructure

GKEH100 GPUsArgoCDMLflowHelmKubernetesDocker
📊

Deep Learning

TensorFlowPyTorchComputer VisionNLPTransformers
🌐

Federated & Decentralized ML

FLock NetworkFederated LearningBlockchainWeb3
💻

Languages & Tools

PythonSQLC/C++RGCPAWSAzureFastAPIGit

// Experience

Where I've built things

Jan 2025 to Present
AI/ML Engineer IV
Digital Currency Group
  • Architected and scaled decentralized AI systems on the FLock Network using Federated ML; forged strategic collaborations with Animoca Brands, Pundi AI, Chasm, and OneKey to build LLM-powered agents for VC analysis, financial trading, sports analytics, and smart contract security.
  • Spearheaded fine-tuning of LLMs (Qwen, LLaMA, Gemma, Phi) and engineered a resilient ML pipeline using Argo CD, GKE (H100 GPUs), MLFlow, Helm, and Pub/Sub for scalable, production-grade training and inference.
  • Designed and deployed VCScout-AI, a LangChain-based agentic RAG pipeline with Qwen-7B, Weaviate vector search, and custom evaluation frameworks to analyze 300+ startup deals, directly driving $450K+ in AI-informed investment decisions.
  • Recognized as Top AI Arena performer; invited speaker at EthCC (Brussels) and NEARCON 2026 (San Francisco), including a lightning talk on training 7B LLMs on edge devices. Featured in the official FLock blog interview and tweet.
Mar 2024 to Dec 2024
AI/ML Engineer
Foundry Digital
  • Spearheaded federated ML on the FLock network for decentralized models on chain; achieved top-performing status seven times across distinct tasks, with 1st place in two major ML hackathons and 2nd in another; mentored junior engineers on MLOps and model serving.
  • Led a technical workshop at RIT on Machine Learning in Crypto, training and deploying production models on FLock and Akash Network.
  • Featured in FLock interviews highlighting innovative work in the network.
Jun 2023 to Mar 2024
Senior ML Engineer
OneBill Software
  • Led ML projects for Sales & Marketing, including Customer Account Clustering (K-Means) and a Product Recommender System (Collaborative Filtering) for 200+ accounts; implemented XGBoost churn prediction, retaining 93% of at-risk customers.
  • Designed profitability, market opportunity, and pricing dashboards driving 20%+ quarterly financial gains; fine-tuned LLMs for industry-specific queries, boosting automated query resolution by 40%.
Jun 2022 to Mar 2023
Deep Learning Engineer
Intel Corporation
  • Optimized TensorFlow on Windows and pioneered an MLOps framework to streamline automation and acceleration of Neural Network benchmark performance on various Intel CPUs.
  • Enhanced convolution operations inference latency by 400% using OneDNN; triaged and resolved critical TensorFlow CPU issues to production readiness.
  • Built and released the TensorFlow binary to the worldwide TensorFlow community in collaboration with Google, achieving 10,000 downloads for TF 2.11 in a single day.
Sep 2021 to Dec 2021
Data Scientist Intern
Intel Corporation
  • Built a telemetry data pipeline (Grafana/Graphite), developed an Isolation Forest anomaly detection system (92% accuracy), and created a BERT-based QA system for technical document retrieval.
Jan 2021 to Aug 2021
Data Analyst Intern
Nokia
  • Designed Power BI dashboards, automated data pipelines (Flow), and contributed to go-to-market strategy solutions leading to a hyperscaler partnership.
Sep 2020 to Dec 2020
Graduate Teaching Assistant
Northeastern University
  • Taught Python, Machine Learning, and Data Analysis to 250 students; led practicum workshops on model development and evaluation; mentored student teams on applied ML projects.
Jun 2017 to Aug 2017
Software Development Engineer Intern
OneBill Software
  • Developed SOAP/REST APIs and contributed to web service design using Postman and Maven.

// Education

Background

Northeastern University, Khoury College of Computer Sciences
Master of Science, Data Science
Boston, MA · GPA 3.8/4.0
Sept 2019 – May 2022
Dayananda Sagar University
Bachelor of Technology, Computer Science and Engineering
Bangalore, India · GPA 8.2/10
Aug 2015 – June 2019

// Achievements

Recognition

Top Performer on the FLock Network July 2024

Achieved top-performing model status 7 times across 7 distinct tasks on the blockchain-based FLock network. Featured internationally at EthCC Brussels and in the official FLock blog interview, tweet, and YouTube segment.
AIA Division Achievement Award, Intel Nov 2022

Recognition for the TensorFlow Windows project, leading to Intel's ownership of the TensorFlow CPU build from Google and the first successful release for the Windows ecosystem.

// Speaking, mentoring & judging

Stages, classrooms, and hackathons

Speaking

NEARCON 2026, San Francisco Feb 2026

Lightning talk on training 7B LLMs on edge devices at the NEAR Foundation conference (Fort Mason), alongside speakers from PyTorch, Gensyn, Akash Network, and Hyperbolic Labs.
EthCC, Brussels July 2024

Invited segment on decentralized AI and federated learning on the FLock network—how on-chain coordination and incentives change who can train models, what “production” means when compute is distributed, and lessons from shipping real federated workloads. Opens the official EthCC recording on YouTube (FLock segment).
Guest lecture, VIT-AP University April 2026

Two-hour session for the School of Computer Science and Engineering on production AI: how LLMs are built and deployed, LoRA and QLoRA, why most models never leave the notebook, and a walkthrough of a production system from DCG.

Mentoring

Mentor, WiCS at ASU x Opportunity Hack Spring 2026

Mentored university teams building real tools for nonprofits in 24 hours: AI architecture, LLM APIs, data modeling, and scoping so teams could ship. Hosted with Women in Computer Science at Arizona State University and Opportunity Hack.

Judging

Judge, Web Data UNLOCKED Hackathon, lablab.ai × Bright Data May 2026

Selected by lablab.ai to evaluate AI agent submissions at the Web Data UNLOCKED Hackathon, a $18,300-prize competition co-hosted by Bright Data. Assessed technical quality, LLM integration depth, and real-world viability of agentic AI systems built on live web data infrastructure.

// Publications & trade articles

Publications and trade articles

Publications

Fine-Tuning Large Language Models in Resource-Constrained Environments: Methods and Trade-offs Under review

This survey examines practical approaches to fine-tuning large language models under realistic hardware limitations. It covers parameter-efficient fine-tuning methods including LoRA and its variants, quantization techniques such as QLoRA and GPTQ, memory optimization strategies, and federated learning approaches for distributed training. The paper synthesizes empirical findings across these methods and proposes a decision framework for selecting appropriate configurations under varying resource constraints. Currently under peer review at PeerJ Computer Science (Q1).

Adaptive Phase-Switching for Communication-Efficient Federated LoRA Fine-Tuning Under review

This paper introduces a bidirectional B-only protocol for federated LoRA fine-tuning that tracks per-round upload and download bytes explicitly, and ReverseAdaptive, an aggregator that switches between FLoRA and FFA-LoRA based on a loss-improvement plateau signal. Experiments across TinyLlama-1.1B and LLaMA-3.2-3B show up to 40.5% measured communication savings with no measurable downstream accuracy cost on MMLU, ARC-Easy, BoolQ, and HellaSwag. Currently under peer review at Transactions on Machine Learning Research (TMLR) (OpenReview #9042).

Trade articles

QLoRA Explained: The Memory Compression Breakthrough May 2026

HackerNoon — NF4 quantization, double quantization, and paged optimizers for memory-efficient LLM fine-tuning; practical memory numbers and when to choose QLoRA vs LoRA vs full fine-tuning.

Peer reviewer

Journal of Supercomputing (Springer Nature) May 2026

Invited peer reviewer selected by the Editor-in-Chief to evaluate original research manuscripts on high-performance computing, distributed AI systems, and machine learning infrastructure for one of Springer Nature's longest-running peer-reviewed computer science publications.

// Projects

Other work

Project

Brain Tumor Detection

Developed a 12-layer TensorFlow CNN using Adam optimizer, Cross-Entropy loss, Dropout, and Batch Normalization, reaching 0.96 sensitivity for detecting tumors in MRI images.

TensorFlowCNNComputer VisionMedical AI
Project

Music Sentiment Analysis

NLP-powered system analyzing sentiment in music lyrics using transformer-based models and custom fine-tuning pipelines.

NLPTransformersPythonSentiment
Project

Recommender Systems (Amazon Reviews)

Built a collaborative filtering system using PCA-reduced user-item matrix, Agglomerative Hierarchical Clustering, and Matrix Factorization, achieving MAE of 0.096.

Collaborative FilteringPCAMatrix FactorizationPython
Project

Imbalanced Datasets: Preeclampsia Prediction

Case study on the PROTECT health database: implemented Bootstrapping, SMOTE, and Semi-Supervised Learning with Autoencoders and classifiers to achieve 96% accuracy in predicting Preeclampsia in pregnant women.

Semi-supervisedSMOTEAutoencodersPython
Project

Diabetes Prediction in R

Trained Logistic Regression, SVM, Random Forest, and XGBoost models; achieved 0.92 sensitivity; handled sparse/imbalanced data using ROSE sampling and Boruta feature selection.

RXGBoostROSEBoruta

// Contact

Let's connect

Feel free to reach out to learn more about me and the work I do. Always happy to chat about AI, engineering, or new opportunities.

Email LinkedIn GitHub Website

Fairport, NY 14450 · franklin.je@northeastern.edu