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Click here or scroll down to respond to this candidate Candidate's Name
Principal Machine Learning Engineer | Senior Data Scientist | Expert In AI, ML And
Data Science
EMAIL AVAILABLE PHONE NUMBER AVAILABLE Minneapolis, Minnesota, Street Address , United States
PROFILE
Proficient in Python, R, TensorFlow, and Keras, with a specialization in deep learning techniques, including
Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Natural Language
Processing (NLP). Skilled in leveraging big data technologies such as Apache Spark, Hadoop, and Kafka.
Experienced in leading diverse projects across various sectors, including travel recommendation systems at
Company A, fitness applications at Company B, and Customer Relationship Management (CRM) systems at
Company C. Adept at deploying applications on cloud platforms like AWS, GCP, and Azure. Driven by a passion
for applying AI to create real-world solutions and enhance user experiences.
PROFESSIONAL EXPERIENCE
09/2021 present Senior Machine Learning Engineer
Ecologix - C2C Contractor
Developed Recommendation System: Created an AI-powered recommendation
engine utilizing TensorFlow and scikit-learn to provide personalized travel
suggestions.
Prediction Models: Employed RNNs and LSTMs to predict popular upcoming travel
destinations based on historical user behavior.
Search Optimization: Enhanced itinerary planning with fine tuned search
functionality using Apache Spark.
NLP Integration: Integrated a chatbot with NLP capabilities for immediate user
support and inquiries.
Data Management: Efficiently managed extensive travel data using PostgreSQL and
optimized data retrieval with Apache Kafka.
Cloud Solutions: Executed cloud-based solutions on AWS, leveraging EC2 and
Lambda for seamless scalability.
Data Visualization: Elevated user experience with interactive data visualizations using
Seaborn and Plotly.
Algorithm Refinement: Applied gradient descent and evolutionary algorithms to
refine travel route suggestions.
Image Categorization: Implemented CNNs for automatic categorization of property
images, enhancing visual search.
Dynamic Pricing Model: Engineered a dynamic pricing model with XGBoost,
considering property features and market demand.
Real-Time Data Manipulation: Leveraged Pandas and Dask for streamlined real-time
data manipulation and property availability checks.
Cloud Storage: Embraced cloud storage solutions, particularly AWS S3, for managing
extensive property media.
Sentiment Analysis: Integrated BERT transformers for understanding and processing
user reviews, extracting sentiment and feedback.
Large Data Processing: Employed Apache Hadoop for processing and analyzing large
volumes of user and property data.
GCP Deployment: Deployed optimized cloud-based solutions on Google Cloud
Platform, including AI Platform for model serving.
Model Regularization: Applied regularization techniques like dropout and early
stopping to prevent model overfitting.
05/2018 08/2021 Machine Learning Engineer
Interclypse - C2C Contractor
NLP System Development: Engineered a state-of-the-art NLP system using deep
learning models, significantly improving customer satisfaction metrics.
Data-Driven Analysis: Conducted in-depth data analysis, providing actionable
insights that boosted customer retention rates.
Chatbot Development: Led the development of a bespoke chatbot, effectively
decreasing the volume of human-agent support requests.
Candidate's Name 1/3
Computer Vision Systems: Designed and implemented computer vision-based
systems for license plate recognition and gesture recognition, enhancing traffic
management and human-computer interaction.
Named Entity Recognition (NER): Created a custom NER system, improving data
accuracy and reducing processing time.
Recommendation Systems: Developed recommendation systems utilizing
reinforcement learning and graph-based algorithms, reducing recommendation
errors and enhancing diversity.
09/2016 07/2018 Computer Vision Developer
Mindgrub Technologies
Recommendation Systems Development: Directed and supervised the creation of
recommendation systems using collaborative filtering techniques, resulting in
enhanced customer satisfaction and increased sales revenue.
Text Classification and Sentiment Analysis: Implemented advanced text classification
and sentiment analysis systems utilizing NLP techniques, significantly improving
document categorization accuracy and marketing effectiveness.
E-commerce Platform Recommendations: Designed and executed a large-scale e-
commerce platform recommendation system, leveraging deep learning models to
boost customer engagement and sales revenue.
Mentorship and Team Collaboration: Provided mentorship to junior engineers,
fostering skill development and maintaining a collaborative work environment.
Continuous Learning: Engaged in continuous learning and exploration of emerging
trends in machine learning and artificial intelligence, actively applying new
knowledge to enhance project outcomes.
Stakeholder Collaboration: Collaborated with stakeholders to align machine learning
initiatives with broader business objectives and goals, facilitating effective project
execution.
Knowledge Sharing: Conducted regular knowledge-sharing sessions within the team,
promoting a culture of continuous learning and innovation.
SKILLS
Machine Learning Artificial Intelligence Natural Langugae Processing
Computer Vision Deep Learning Data Gathering
Scrapy Data Analysis & Visualization Python
Selenium Data Scraping Rotating Proxies Scikit-learn
Django Sockets TensorFlow
TensorRT Keras MLOps
Image Processing PyCharm LSTM
Fast API Numpy Time Series Analysis
Shell & AWK C/C++ Pandas
Hadoop Docker ElasticSearch
CI/CD Pipelines Automated Machine Learning RESTful APIs
(AutoML)
PROJECTS
Shelf Detection
The Shelf Detection project is a Python-based application designed to automate the process of identifying and
cataloging items on a shelf through uploaded images. Leveraging sophisticated image processing techniques,
including image stitching, this project provides a comprehensive view of the shelf's contents.
Ivision Tennis
iVision Tennis is an innovative Python-based project that leverages deep learning techniques to transform real-
time tennis streams into highly realistic, simulated content. By employing advanced image synthesis algorithms,
the project creates a convincing virtual representation of the game, enhancing the viewing experience for tennis
enthusiasts.
Candidate's Name 2/3
Emotion Detector
The Emotion Detector project is a Python-based application that employs transfer learningto accurately identify
and classify emotions from images or video frames. By leveraging a pre-trained deep learning model, this project
enables robust emotion recognition in real-time.
Virtual Try-on Room
The Virtual Try Room is a Python-based project utilizing machine learning to virtually fit clothing onto user
uploaded images. This technology offers an interactive, lifelike preview of clothing items on the user's image,
enhancing their ability to make informed online purchase.
Social-Influencers
Developed matching algorithms to identify relevant connections and associations within the scraped data. Trained
a classifier to categorize creators into their primary niches based on their content and profiles. Collaborated with a
team to design and build an analytics dashboard for data visualization and insights. Utilized GPT-3 to generate
biographies for Instagram profiles, enhancing profile descriptions. Leveraged Python for Natural Language
Processing (NLP) and Computer Vision tasks. Employed Elasticsearch and Kibana for data indexing, searching,
and visualization within the project
Sentiment Analysis
Implemented a sentiment analysis system to automatically classify and analyze the sentiment expressed in textual
data. Utilized natural language processing (NLP) techniques and machine learning algorithms to train the model
on diverse datasets. The project involved data preprocessing, feature extraction, and model training using Python
and popular libraries such as NLTK and Scikit-learn. Implemented deep learning models, including recurrent
neural networks (RNNs) and transformers, to enhance accuracy and handle complex language nuances. Integrated
the system with web applications and APIs to enable real-time sentiment analysis.
EDUCATION
Master in Computer Science
San Diego State University
Candidate's Name 3/3
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