Emmanuel K.

Key Skills

Software

Appen

Labelbox

Data Annotation Tech

Other

Don't disclose

Top Subject Matter

Computer vision dataset labeling for AI training

Video segmentation and QA for AI training

AI agent integration for trading intelligence (annotation-adjacent)

Top Data Types

Image

Video

Text

Top Task Types

Bounding Box

Point/Key Point

Object Detection

Text Summarization

Text Generation

Evaluation/Rating

Data Collection

Prompt + Response Writing (SFT)

Segmentation

Fine-tuning

Freelancer Overview

AI Data Annotator — Image/Video Labeling and QA (CrowdGen by Appen). Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Don't disclose. Education includes Bachelor of Engineering, University of Uyo (2020) and Blockchain Basics Certification, Cyfrin Updraft (2025). AI-training focus includes data types such as Image, Video, and Computer Code and labeling workflows including Object Detection, Segmentation, and Fine-tuning.

Labeling Experience

Solo Builder — ARGUS AI trading terminal (pre-launch)

Don't discloseFine-tuning

Built an AI agent and on-chain intelligence workflow as part of a Solana dApp project focused on real-time trading assistance. Integrated labeled/validated insights into the application logic to support guard/scanning and automated trade decision features. Applied output validation concepts to ensure the system’s results align with intended use cases.• Integrated Jupiter Ultra API for swaps and trade execution• Implemented security screening and trigger order automation• Used WebSocket subscriptions and on-chain data sources for intelligence feeds• Designed ARGUS as a data-first tactical UX bridging AI and DeFi

2020 - Present

AI Data Annotator — Segmentation and Validation (CrowdGen by Appen)

OtherVideoSegmentation

Executed video labeling and segmentation work to produce high-quality training data for AI systems. Applied semantic segmentation and related annotation types to support model learning across varied scenarios. Carried out quality assurance including validation of AI outputs and visual context checks.• Annotated semantic segments for video frames/datasets• Added bounding box, keypoint, and polygon annotations where required• Performed spatial and lighting analysis for QA• Maintained consistent structured contributions across tasks

Not specified

AI Output Validation & QA (as part of AI Data Annotation)

LabelboxText

Validated and quality-checked AI outputs produced by annotation or model pipelines. Applied analysis techniques to assess visual context and interpret intent and narrative. Performed spatial and lighting checks as part of the QA and quality assurance process. • Used visual context interpretation to confirm label relevance. • Assessed intent and narrative consistency for correctness. • Checked spatial arrangement and lighting attributes during QA. • Performed output validation to ensure reliable training data.

Not specified

AI Data Annotator (Data Annotation)

OtherImageBounding BoxObject Detection

Performed large-scale image and video labeling to support computer vision training. Completed object detection, semantic segmentation, and structured annotations such as bounding boxes, keypoints, and polygons. Applied consistent guidelines to ensure high-quality, model-ready datasets. • Conducted scene and mood classification and temporal sequence labeling. • Maintained high inter-annotator agreement through strict adherence to labeling rules. • Validated AI outputs and performed visual context QA checks. • Used multiple annotation tools to deliver consistent structured results.

Not specified

Education

Work History