Alex Maina

Key Skills

Software

Appen

CloudFactory

Data Annotation Tech

Google Cloud Vertex AI

HiveMind

LabelImg

Label Studio

OneForma

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Data Collection

Diagnosis

Polygon

Polyline

Question Answering

Freelancer Overview

As a dedicated Digital Freelancer with over 4 years of hands-on experience in AI data labeling, I specialize in preparing high-quality datasets for advanced machine learning models. My expertise spans critical domains including Computer Vision (CV), where I've meticulously annotated imagery for autonomous systems and retail analytics, and Natural Language Processing (NLP), encompassing tasks from sentiment analysis to complex text classification. I possess a strong command of diverse annotation techniques for image, video, and text data, ensuring precision and consistency crucial for model performance. My background extends to Large Language Model (LLM) evaluation and text generation, where I assess AI outputs for relevance, fluency, and adherence to guidelines. This comprehensive skill set, combined with my Bachelor of Science in Information Technology and an AI/ML Data Annotation Certification, allows me to approach data quality with a strong technical foundation and a keen eye for detail. I am adept at collaborating with AI developers to refine guidelines and consistently deliver high-volume, accurate data that drives impactful AI development.

Labeling Experience

Remotasks: Semantic Segmentation for Environmental Analysis

Scale AIImagePolygonSegmentation

Performed pixel-level semantic segmentation on aerial and satellite imagery. Accurately outlined and classified different environmental features such as land cover (e.g., water bodies, vegetation, buildings, roads) to create detailed maps for AI analysis. This precise annotation supported projects aimed at environmental monitoring, urban planning, and agricultural assessment, requiring meticulous attention to detail at a granular level.

Performed pixel-level semantic segmentation on aerial and satellite imagery. Accurately outlined and classified different environmental features such as land cover (e.g., water bodies, vegetation, buildings, roads) to create detailed maps for AI analysis. This precise annotation supported projects aimed at environmental monitoring, urban planning, and agricultural assessment, requiring meticulous attention to detail at a granular level.

2019 - 2024

Education

Work History