Ricky Martin

Resume

I’m a Machine Learning Engineer with a track record of building and deploying scalable, production-grade AI systems. From GPU-accelerated LLM agents to time-series anomaly detection microservices, I design end-to-end solutions that save time, cut costs, and make impact. Whether it’s forecasting financials, classifying disease images, or securing infrastructure, I ship fast, think critically, and demo like a pro.

AI/ML Engineer at DCCA

July 2023 - Present

* Led development and deployment of End-to-End Internal Langgraph Agentic RAG ChatGPT Clone deployed on EC2 instances allowing users to login, access history, and chat with uploaded files. Built using Bedrock, FIASS Vector Database, and Streamlit.
* Hosted and operationalized GPU accelerated LLMs (Llama2, Llama3) enabling rapid delivery of POCs for 3 key organizational applications: a long-form Document summarizer, an internal secure SQL Agent, and a Fine-Tuned report generation tool.
* Developed a sliding-window forecasting model using Distributed XGBRegressor to predict Medical Financial Data 2 quarters into the future resulting in several thousands in savings by allowing early price change preparation across the organization.
* Prepared and presented demos on AI/ML solutions to hundreds of individuals/shareholders from various departments.

ML Engineer at Ardent

January 2022 - May 2023

* Partnered with security team to build end-to-end time series (ARIMA, Kalman Filter, Prophet) microservices to monitor real-time login attempts in Splunk, predicting anomalies and reducing threat targeting time by 90%
* Conceived, Designed, Trained and Deployed robust scalable ML code pipeline using technologies like GraphQL, Python, PyTorch, and AWS to serve 20+ REST Api endpoints for predicting errors in microservice health data to shine light on root cause analysis.
* Devised classification algorithm for heavily skewed large-scale dataset using Textract OCR, common NLP techniques, PySpark, and XGBoost, achieving best PRAUC score of .91 after employing SMOTE techniques. Deployed/Monitored via AWS SageMaker

Data Scientist at Redica Systems

October 2020 - October 2021

* Built and Iterated upon production-level semi-supervised DBSCAN algorithm in Python, utilizing NLP techniques such as TF-IDF through NLTK to segment documents. Streamlining the manual labeling process saving hundred of hours.
* Improved precision and recall of supervised deep learning model by 10% using feature engineering, enhancing model effectiveness.
* Collaborated with cross-functional teams to define and report on client KPIs, and present impact of data-driven solutions to them.

Data Science Researcher at UCSD Bio Inspired Robotics and Design Lab

May 2018 - June 2021

* Spearheaded the construction of 5 distinct ML on edge iterations gathering/building large datasets from live sensor data and using Python/C++ to process, transform, interpret, and train LSTM and other Deep Learning Models on local GPUs using CUDA.
* Built a customized GAN based model to create synthetic Data from collected sensor data for better model training.
* Devised Random Forest parameter tuning pipeline that utilized impactful features for 50% faster classification.

Data Science Intern at GoSite

October 2019 - October 2020

* Programmed and Deployed recommendation systems utilizing past converstion data on new Leads. Worked to improve recommendations further with each iteration.
* Built a Google, Yelp, YellowPages, and Angies list webscraper to gather new leads for sales teams. Resulting in thousands of New noteworthy leads.