Felipe Batista
Verified Expert in Engineering
Machine Learning Engineer and Full-stack Developer
Felipe has 8+ years of experience in machine learning and full-stack software development. He's currently focused on cutting-edge technologies such as TensorFlow, Keras, PyTorch, OpenCV, and most of the Python data science stack. He is an AWS-certified solutions architect skilled in implementing deep learning models from research papers with a focus on computer vision and reinforcement learning.
Portfolio
Experience
Availability
Preferred Environment
GitHub, PyCharm, Linux, MacOS
The most amazing...
...project I've implemented was an AI SaaS app that was able to serve over 100,000 users, leveraging complex user behavior analytics to improve the core offering.
Work Experience
Founder and Software Engineer
Totem AI
- Developed and scaled an image generation service to 1+ million users and built a sophisticated image evaluation pipeline to improve renders, reduce generation time, and provide high-quality control.
- Built generative AI models for text and image generation, utilizing technologies like Stable Diffusion, GPT (and similar models), retrieval augmented generation (RAG), and diffusion models.
- Gained proficiency in analyzing customer behavior data within the SaaS domain to identify key churn drivers, utilizing advanced analytics and user segmentation techniques. Developed machine learning models to predict churn and identify risky behavior.
- Developed comprehensive business intelligence solutions, focusing on data-driven dashboards and financial modeling for a mid-size agriculture conglomerate with over 60 million in revenue.
- Developed image classification pipelines using convolutional neural networks (CNNs, data augmentation, and transfer learning) for real-time object detection, face classification/recognition, and semantic segmentation.
- Architected digital signal processing pipelines for healthcare (freezing of gait detection for Parkinson's disease patients using sensor data).
- Developed real-time machine learning models for fantasy football, including analysis of optimal lineup selections.
- Implemented scientific papers containing state-of-the-art research related to computer vision, digital signal processing, time-series modeling for financial markets, and NLP for data enrichment.
Data Scientist and Full-stack Software Engineer
ShopYak
- Developed a neural network and Q-learning (reinforcement learning) to perform automated A/B testing on different website layouts for eCommerce stores. The objective was to optimize conversions by adjusting the layout, fonts, and colors for each store.
- Designed and developed portions of the front end using AngularJS and SCSS. Set up the build process using Grunt.
- Implemented several PowerBI dashboards to keep track of KPIs and general user behavior (using both DB and Google Analytics integrations).
- Developed a significant portion of the back end using Node.js, Express, and PostgreSQL.
- Integrated Stripe (regular and connect) both in the front and back end.
- Deployed on AWS with HTTPS, Amazon CloudFront, Amazon S3, and Elastic Load Balancing (ELB).
Experience
Large-scale Deep Learning-based Video Analytics Pipeline for a United Nations Project
DSP Using Multiple Deep Learning Architectures (CNNs, LSTM, GRU)
Over the course of the project, I:
1. Reviewed several papers containing the state-of-the-art methods for NILM.
2. Optimized available models to perform an initial POC.
3. Explored different model architectures on a specific setting defined by the client, including:
a. Parallel CNNs
b. Parallel CNNs with LSTMs
c. CNNs with LSTM (bidirectional)
d. CNN with GRU
Models were optimized, and ultimately, the best model was chosen based on the results of a cross-validation routine.
Deliverables were both Jupyter Notebooks and Python Scripts.
Image Classification Pipeline with TensorFlow/Keras
Tested several different model architectures (including multi-input models with both images and bounding boxes).
Settled on a fine-tuned VGG16 network.
The pipeline included data augmentation, cross-validation, visualization of accuracy and loss across epochs (and sample results).
DSP/DL for Freezing of Gait Detection (with POC Mobile App)
Replicated state of the art medical scientific papers using Python, Sklearn, Tensorflow, Keras, and Jupyter in order to obtain well-defined baselines.
After in-depth research of DSP techniques for anomaly detection, I implemented a few Deep Learning Model architectures never before applied to this specific domain.
Performed cross-validation to assess model performance focusing on the model's generalization potential. The model was trained using the data of 80% of the patients and tested on the data for the remaining patients.
Examples of Models implemented on this project:
CNNs
Deep Conv LSTM
Parallels CNNs w/ LSTM
In order to test the model, I implemented a simple Android application that used the trained model to make a live Freezing of Gait inference based on the phone's accelerometer data. The model was converted to CoreML for future iOS app development.
Other relevant work includes handling of class imbalance by adjustment of the loss function of the deep learning models, hyperparameter optimization using GridSearch, and simulation of the effect of different windows sizes on model performance.
Skills
Languages
Python, JavaScript, SCSS, R, C++
Frameworks
LlamaIndex, Express.js, Flask, Angular
Libraries/APIs
NumPy, Pandas, Keras, TensorFlow, Scikit-learn, OpenCV, PyTorch, Node.js, Amazon Rekognition, SpaCy
Tools
Microsoft Excel, Amazon SageMaker, Git, Microsoft Power BI, PyCharm, GitHub, Grunt
Paradigms
Data Science, Agile Software Development
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Google Cloud Platform (GCP), MacOS, Linux
Other
Machine Learning, Computer Vision, Deep Learning, Artificial Intelligence (AI), Diffusers, Stable Diffusion, ControlNet, Retrieval-augmented Generation (RAG), Image Generation, Replicate, ChatGPT, Team Leadership, Generative Artificial Intelligence (GenAI), Chatbots, OpenAI GPT-4 API, OpenAI GPT-3 API, LangChain, Analytics, Natural Language Processing (NLP), Econometrics, Computational Finance, Statistics, Generative Pre-trained Transformers (GPT)
Storage
Google Cloud, MongoDB, PostgreSQL
Education
Bachelor of Science Degree in Economics with a Focus on Econometrics and Computational Methods
UFMG - Belo Horizonte, Brazil
Certifications
AWS Certified Solutions Architect Associate
AWS
Deep Learning Specialization
Coursera
Image and Video Processing
Coursera
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
Share your needs
Choose your talent
Start your risk-free talent trial
Top talent is in high demand.
Start hiring