Key Skills

Software

Data Annotation Tech

Google Cloud Vertex AI

Labelbox

OpenCV AI Kit (OAK)

Scale AI

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Video

Top Task Types

Action Recognition

Classification

Computer Programming Coding

Fine Tuning

Segmentation

Freelancer Overview

As an AI Engineer, I have substantial experience preparing diverse datasets for machine learning and deep learning models. My expertise spans the entire data pipeline, from initial cleaning and preprocessing to feature engineering and the creation of labeled datasets. I've worked with time series data for financial market prediction, developing models like LSTMs and Transformers. Furthermore, I've handled multimodal data (video and sensor readings) for a road auscultation project, building a pipeline for object detection and classification of road defects. This involved synchronizing data streams, annotating video frames, and cleaning sensor data. I'm proficient in ensuring data quality and consistency, crucial for effective AI training.

Labeling Experience

Financial Time Series Prediction

Google Cloud Vertex AITextClassificationMapping

Prepared financial time series data for training various prediction models, including LSTMs, Transformers, and traditional machine learning algorithms. My responsibilities encompassed a comprehensive data preparation pipeline. This included rigorous data cleaning to address outliers and inconsistencies, feature engineering to create relevant technical indicators (e.g., moving averages, RSI, MACD), handling missing data points using appropriate imputation techniques, and transforming the raw data into labeled datasets suitable for supervised learning. A key focus was ensuring data quality and consistency to enable robust model training and reliable evaluation of predictive performance.

Prepared financial time series data for training various prediction models, including LSTMs, Transformers, and traditional machine learning algorithms. My responsibilities encompassed a comprehensive data preparation pipeline. This included rigorous data cleaning to address outliers and inconsistencies, feature engineering to create relevant technical indicators (e.g., moving averages, RSI, MACD), handling missing data points using appropriate imputation techniques, and transforming the raw data into labeled datasets suitable for supervised learning. A key focus was ensuring data quality and consistency to enable robust model training and reliable evaluation of predictive performance.

2024 - 2024

Education

Work History