Machine Learning Engineer
Resume:
R Y A OW N P A L M E R
M A C H I N E L E A R N I N G E N G I N E E R
C O N T A C T
****.**********@*****.***
Fairfax, VA 22030
https://www.linkedin.com/in/rya
n-palmer-5a314a130/
GEORGIA INSTITUTE OF TECHNOLOGY
Bachelor’s Degree in CS
2008 - 2012
2012 - 2014
GEORGIA INSTITUTE OF TECHNOLOGY
Master’s Degree in CS
E D U C A T I O N
S K I L L S
Python
C
C#
R
Matlab
Artificial Intelligence (AI)
Semantic Kernel
Machine Learning (ML)
Optimization
Generative AI
Computer Vision
Natural Language Processing
LLM
P R O F I L E S U MMARY
Accomplished Machine Learning Engineer with a rich background in AI innovation, blending cutting-edge technology and robust reliability to push the boundaries of artificial intelligence. With a strong commitment to collaborative teamwork, I am eager to contribute my deep expertise in AI to drive the development and success of projects, ensuring they not only meet but surpass high-quality standards within stringent timelines.
WORK E X P E R I E N C E
Senior ML Engineer JUN 2021 - MAY 2024
Dogwood Logic, Inc. Blacksburg, Virginia
Developed Medical Imagery Detecting System which run image segmentation on Radiology and Pathology images to find anomaly, tumor, lesion and grade it, compared it with previous images, showed disease progression and provide medication recommendation using SAM using AWS Sagemaker.
Introduced Customized AI Chatbot to intelligently respond and manage CRM without human intervension in Banking deployed on AWS.
Expertise in training and deploying CNN, RNN and LSTM models on Azure using PyTorch, Tensorflow and other Deep Learning Frameworks.
Guiding the creation of comprehensive and relevant content that addresses the interests and questions of the target audience, making the website more authoritative and relevant to search queries.
Using Spark, Apache KAFKA, Python and Pytorch, developed prototypes for real-time removal of rain droplets from side view cameras.
Implemented ML application using Django Rest Framework and deployed using AWS elastic container.
Customized an NLP system that automatically classified 7.5k emails as spam or advertising email using 11+ natural language processing methods.
Identifying and incorporating relevant keywords from the semantic kernel into web content, titles, meta descriptions, headers, and URLs to improve search engine rankings and visibility.
Machine Learning Engineer JAN 2019 - MAY 2021
Algo 8 AI, Houston, Texas
Participated in developing a computer vision training pipeline with deep learning models for intelligent quality control solutions of medical products and cosmetics using Python, Django, Tensorflow and Flask.
Customized the framework for CCTV surveillance system which detected and tracked objects in real time frames for client’s requirements, reached 95% of accuracy.
Introduced AI technology to healthcare management systems to improve patient care, diagnose diseases and analyze medical images such as Xrays, MRIs using Python, Django and Tensorflow with Fast API.
Built a real-time object detection model and pipeline using Kubernetes for CI/CD, YOLOv8, OpenCV and other Python libraries.
Developed and automated a time-series anomaly detection machine learning pipeline using Kubernetes for hyperparameter tuning and for AutoML.
Developed commodity price forecasting application using LSTM. Designed, implemented, and tested high-precision algorithm for pedestrian detection using deep neural networks and performance evaluation for varying dropouts.
Designed an algorithm that detects lane marking in real-time camera feed for use by the vehicle tracking system and implemented with Python/OpenCV/Pytorch.
Developed a customized deep learning model to detect and track vehicles in video feeds from cameras mounted on trucks, using Tensorflow and Python.
Implemented web application with AI model for collecting, storing, analyzing and detecting the necessary information and images for sports card.
Developed the project which was to train the several models for the emotion estimation of facial expressions and to evaluate and optimize hyparams of each model's capability with CNN & Scikit- learn using inception model for the feature extraction. Designed and Implemented several image/video processing algorithms which detect, recognize, count and segment special objects from images, videos and cameras.
Pattern Recognition
Speech Recognition
Statistics, Regression
PSO(Particle Swarm Optimization)
GA(Genetic Algorithm)
Deep learning models
SAM (Segment Anything Model)
Yolo serials
CNNs
R-CNNs
ViT
Grounding
DINO
RNN
Transformer
Stable diffusion,
BERT
OpenAI
OpenCV
OSINT
CUDA
NumPy
Pandas
Matplotlib
Seaborn,
Scikit-learn
Keras
Tensorflow
Pytorch
Pytorch Lightning
NLTK
Spacy
Gensim
fastText
Standford toolkit(Glove)
Machine Learning Engineer JAN 2017 - DEC 2018
Tchek.ai, Boston, Massachusetts
Researched, implemented and evaluated SOTA Deep Learning models and rapid software prototypes to solve problems in machine learning and computer vision.
Developed novel algorithm which includes two-step process of accurately recognizing smart license plate using SOTA real time object detection followed by verification process which improves confidence of prediction.
Researched, designed and implemented machine learning models with End-to-End Bayesian Segmentation Network which resulted in savings of $40K per year.
Prepare training dataset for machine learning models and construct training and inference modules using machine learning tools and frameworks.
Developed a customized algorithm to detect changes in remote and vulnerable places seen using fixed cameras and send alert messages when a person or a vehicle is detected.
Data analysis and model training experience with hyperspectral data from drones in GeoTiff format.
Developed a customized neural network-based anomaly detection system that reduces 83% of false positives with backend API and web framework.
Optimized computer vision models with custom dataset for faster execution while maintaining accuracy through code optimization techniques such as loop unrolling and vectorization of loops. Managed full software development life cycle(SDLC) and built customized models for scientific 2D imaging analysis systems. Software Developer SEP 2014 - OCT 2016
Arayr Consulting, Washington D.C. Metro Area
Participated in developing a computer vision training pipeline with deep learning models for intelligent quality control solutions of medical products and cosmetics using Python, Django, Tensorflow and Flask.
Customized machine learning algorithms to classify objects and captured by thermal imaging cameras used in robotic vacuum cleaners, using OpenCV and Python.
Built a real-time image processing pipeline that utilized deep learning techniques on GPU servers.
Developed computer vision algorithms to detect and track objects of interest in video streams using OpenCV. Implemented customized machine learning models for object detection, tracking, pose estimation and recognition from images/videos.
Performed data pre-processing and argumentation using Python. Developed Face Detection and Recognition Engine with custom dataset for Employee Management System and made RESTful APIs for Web Services.
Hadoop
Apache KAFKA
PySpark
AWS
GCP
Microsoft Azure
AWS SageMaker
Kubernetes
Django
Flask
React.js
RESTfulAPIs
Node.js
Fast API
MongoDB
PostgreSQL
MySQL
Git
Github
Jira
Bitbucket SDLC
Punctuality
Problem solving
Public Presentation skill
Time Management
Planning
Persuasive
Collaborative spirit
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