Machine Learning Computer Vision

J ones Kengie

**** * **** *** *****, FL ***** • 720-***-****• ***********.**@*****.***

Passionate AI/ML Engineer with 8+ years of experience in Machine Learning (ML) model design, fine-tuning, testing and deployment.

Expertise with Computer Vision, Generative AI (GenAI), Natural Language Processing (NLP) with Large Language Models

(LLM), Data Analysis, and most importantly delivering AI/ML solutions across diverse industries with GCP AI services as well as AWS. Highly experienced in programming with Python (TensorFlow/Keras), Matlab as well as Web development. Proficient in Git version control for collaborative software development. Possess passion for AI with comprehensive knowledge of machine learning concepts and other related technologies. Excellent communication and presentation skills in describing technical issues with co-workers and managers. S k i l l s

Machine Learning (ML) • Deep Learning (DL) • Computer Vision • Generative AI (GenAI) • Natural Language Processing

(NLP) • Large Language Models (LLM) • Financial Forecasting • C/C++ • Python • PyTorch • Tensorflow • Keras • Pandas

• Numpy • Java • Android • HTML • CSS • JavaScript • PHP • Node.js • React.js • Angular.js • Vue.js • MySQL • MongoDB • MLflow • Git • GCP • AWS • Azure

E x p e r i ence

01/2021 – PRESENT

Senior Machine Learning Engineer Innowise Group New York, NY Remote

• Extensive experience in developing and fine-tuning large language models (LLMs) like GPT, BERT, and FLAN-T5 for specific tasks, such as healthcare question-answering, using parameter-efficient fine-tuning (PEFT) techniques.

• Proficient in implementing MLflow for comprehensive model lifecycle management, including continuous tracking of performance metrics and model versioning, ensuring robustness and reproducibility.

• Skilled in deploying machine learning models on cloud platforms like AWS, leveraging services such as Amazon SageMaker and AWS Glue for optimized data analysis and model deployment pipelines.

• Adept in utilizing Python libraries like pandas and PyTorch for data preprocessing, model development, testing, and deployment, covering the entire machine learning lifecycle.

• Expertise in crafting effective prompts and prompt engineering techniques to elicit desired responses from LLMs, tailored to specific use cases and requirements.

• Familiarity with techniques like in-context learning, prompt tuning, and prompt ensembling to enhance the performance and generalization capabilities of LLMs.

• Experience in evaluating and mitigating potential biases and safety concerns associated with LLMs, ensuring responsible and ethical deployment of these models. 02/2017 – 10/2020

AI/ML Engineer Roonyx Petersburg, FL Hybrid

• Developed a state-of-the-art convolutional neural network (CNN) model in healthcare innovation, achieving an impressive 97% accuracy in classifying X-ray images, revolutionizing medical imaging diagnostics and setting a new standard for precision in healthcare analytics using PyTorch framework.

• Optimized machine learning algorithms, especially CNNs, for enhanced performance, extensively testing them across diverse real-world scenarios.

• Developed a Machine Learning tutoring web app with React frontend, Django backend, enabling interactive parameter visualization to enhance junior developers' understanding of machine learning.

• Proficiently utilized Git version control to ensure seamless collaboration and efficient management of project codebase.

09/2015 – 12/2016

Machine Learning Engineer Cubix West Palm Beach, FL On-site

• Contributed to developing a TensorFlow-based image classification system for botanical species, enhancing skills in image analysis and innovative technology.

• Implemented machine learning models on a mobile application to enable offline usage without GPU support or internet access, optimizing performance for resource-constrained environments.

• Designed and trained state-of-the-art Machine Learning (ML) models for Natural Language Processing (NLP) tasks, such as sentiment analysis, named entity recognition, and machine translation, achieving high accuracy and performance through meticulous model architecture design and training.

• Participated in optimizing model architectures and hyperparameters, also effective algorithm to achieve superior performance metrics in machine learning tasks with Python and PyTorch as well as Tensorflow.

• Experienced in leveraging Machine Learning (ML) methodologies to construct phonetic representations, involving tasks such as feature extraction, modeling, and analysis to effectively capture and represent speech patterns and phonetic nuances.

05/2015 – 09/2015

Intern Infotech Gainesville, FL On-site

• As an intern, learned how to bridge the gap between academic knowledge and real-world application.

• Engaged in learning diverse front-end and back-end web technologies, with a notable emphasis on SQL databases, expanding my skills to contribute effectively to the development of comprehensive web applications capable of managing complex data structures with precision and efficiency. E ducation

05/2014 – 05/2015

Master’s degree Computer and Information Science University of Florida

• Induction Motor Fault Diagnosis using Deep Learning 09/2010 – 05/2014

Bachelor’s degree Computer and Information Science University of Florida

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