Ramiro Vasquez

Key Skills

Software

CVAT

SuperAnnotate

Label Studio

Other

Top Subject Matter

Computer Vision and Medical Imaging

Security and Surveillance AI

Industrial Computer Vision and Document Understanding

Top Data Types

Image

Video

Audio

Top Task Types

Bounding Box

Tracking

Classification

Transcription

Segmentation

Freelancer Overview

Data Labeling Specialist — Computer Vision (Image & Video Annotation). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include CVAT, SuperAnnotate, and Label Studio. Education includes Master of Science, Universidad Católica de Murcia UCAM (2024) and Certificate in Data Analytics, Pontificia Universidad Javeriana (2023). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Bounding Box, Tracking, and Classification.

Labeling Experience

Data Annotation Project Manager — Full Labeling Lifecycle & Validation

ImageSegmentation

Managed the full lifecycle of annotation projects, from task definition to dataset export and quality validation. Implemented QA/QC protocols including consensus voting, adjudication, and benchmarking against gold-standard datasets. Authored guidelines and integrated annotation with MLOps to improve throughput and quality. • Designed annotation pipelines and reviewed inter-annotator agreement. • Managed dataset versioning for AI development traceability. • Integrated model-assisted and active learning workflows. • Standardized ontology and labeling rules for annotation teams.

Managed the full lifecycle of annotation projects, from task definition to dataset export and quality validation. Implemented QA/QC protocols including consensus voting, adjudication, and benchmarking against gold-standard datasets. Authored guidelines and integrated annotation with MLOps to improve throughput and quality. • Designed annotation pipelines and reviewed inter-annotator agreement. • Managed dataset versioning for AI development traceability. • Integrated model-assisted and active learning workflows. • Standardized ontology and labeling rules for annotation teams.

2022 - 2023

Data Labeling Specialist — Audio & Speech Data Annotation

OtherAudioTranscription

Annotated audio datasets by transcribing and time-stamping recordings for ASR model training. Speaker diarization, emotion labeling, and environmental noise classification were conducted across multilingual datasets. Quality review processes ensured accurate labeling for model training and evaluation. • Processed audio in Spanish, English, and Italian for ASR. • Labeled speaker identities, emotion, and environmental sounds. • Flagged edge cases like mispronunciations and overlapping speech. • Performed QA/QC on annotated audio batches.

Annotated audio datasets by transcribing and time-stamping recordings for ASR model training. Speaker diarization, emotion labeling, and environmental noise classification were conducted across multilingual datasets. Quality review processes ensured accurate labeling for model training and evaluation. • Processed audio in Spanish, English, and Italian for ASR. • Labeled speaker identities, emotion, and environmental sounds. • Flagged edge cases like mispronunciations and overlapping speech. • Performed QA/QC on annotated audio batches.

2022 - 2023

Data Labeling Specialist — Operational Efficiency & Document Tasks

Label StudioImageClassification

Labeling was performed on time-series visual data for predictive maintenance and industrial vision applications. Equipment states, defect types, and production line events were annotated to facilitate predictive analytics pipelines. OCR and document understanding data were labeled for tasks including form field extraction, table structure annotation, and layout segmentation. • Labeled geospatial and temporal event data for route optimization. • Supported datasets for food delivery and operational efficiency analytics. • Enhanced industrial safety model datasets with detailed event labeling. • Developed workflows to standardize document-level annotations.

Labeling was performed on time-series visual data for predictive maintenance and industrial vision applications. Equipment states, defect types, and production line events were annotated to facilitate predictive analytics pipelines. OCR and document understanding data were labeled for tasks including form field extraction, table structure annotation, and layout segmentation. • Labeled geospatial and temporal event data for route optimization. • Supported datasets for food delivery and operational efficiency analytics. • Enhanced industrial safety model datasets with detailed event labeling. • Developed workflows to standardize document-level annotations.

2022 - 2023

Data Labeling Specialist — Surveillance & Tracking Projects

SuperannotateVideoTracking

Surveillance and tracking datasets were annotated for person re-identification and cross-camera tracking. The annotation process managed edge cases such as occlusion, scale variation, and low resolution. Anomaly detection was performed by marking unusual behaviors, intrusion events, and zone violations. • Applied ontologies for crowd density estimation and flow direction. • Enhanced datasets for security and operational monitoring applications. • Ensured annotation consistency on continuous video feeds. • Managed quality control for surveillance data labeling.

Surveillance and tracking datasets were annotated for person re-identification and cross-camera tracking. The annotation process managed edge cases such as occlusion, scale variation, and low resolution. Anomaly detection was performed by marking unusual behaviors, intrusion events, and zone violations. • Applied ontologies for crowd density estimation and flow direction. • Enhanced datasets for security and operational monitoring applications. • Ensured annotation consistency on continuous video feeds. • Managed quality control for surveillance data labeling.

2022 - 2023

Data Labeling Specialist — Computer Vision (Image & Video Annotation)

CVATImageBounding Box

Large-scale image datasets were annotated for object detection, instance segmentation, and classification tasks. The work included using bounding boxes, polygons, and keypoints across various environments and lighting conditions. Video sequences were labeled for multi-object tracking and re-identification with consistent track IDs across frames. • Annotated images and videos for action recognition and activity detection. • Performed dense pixel-level semantic segmentation for medical imaging datasets. • Contributed to pose estimation and gesture recognition through anatomical keypoints labeling. • Supported datasets for operational monitoring and industrial safety scenarios.

Large-scale image datasets were annotated for object detection, instance segmentation, and classification tasks. The work included using bounding boxes, polygons, and keypoints across various environments and lighting conditions. Video sequences were labeled for multi-object tracking and re-identification with consistent track IDs across frames. • Annotated images and videos for action recognition and activity detection. • Performed dense pixel-level semantic segmentation for medical imaging datasets. • Contributed to pose estimation and gesture recognition through anatomical keypoints labeling. • Supported datasets for operational monitoring and industrial safety scenarios.

2022 - 2023

Education

Work History