Ayokunnumi M.

Key Skills

Software

OneForma

Top Subject Matter

Computer Vision / RLHF Training Data

Computer Vision Annotation

NLP Training Data (Intent, NER, Sentiment)

Top Data Types

Image

Video

Text

Document

Top Task Types

Bounding Box

Object Detection

Freelancer Overview

Data Annotator & AI Training Specialist (Outlier.ai). Core strengths include Outlier, OneForma, and Handshake AI. Education includes Bachelor of Science, University of Lagos (2021). AI-training focus includes data types such as Image, Video, and Text and labeling workflows including Bounding Box, Object Detection, and Entity (NER).

Labeling Experience

Data Annotator & AI Training Specialist (Outlier.ai)

ImageBounding Box

Performed large-scale computer vision annotation including bounding boxes, instance segmentation, and keypoint labeling to support ML training pipelines. Validated and converted dataset files across JSON/CSV/XML formats, ensuring schema compliance for downstream model training. Applied ML-library-assisted data manipulation (NumPy/Pandas) within TensorFlow/PyTorch-backed workflows for consistent training inputs. • Annotated 50,000+ image/video samples for ML model training. • Maintained annotation quality scores consistently above 97% across 15+ concurrent projects. • Ran Python scripts to clean, validate, and batch-process annotation exports in JSON and CSV. • Supported RLHF rating/ranking of AI outputs to improve response quality and alignment.

2023 - Present

Data Annotation Specialist (OneForma/Centific)

OneFormaImageObject Detection

Completed object detection (bounding boxes), human pose estimation (keypoints), and polygon segmentation annotations for computer vision datasets. Prepared and validated structured XML/JSON outputs to match client ML pipeline specifications for each annotation batch. Organized labeled outputs into consistent train/validation splits with aligned folder hierarchies and metadata records. • Used OpenCV to resize, normalize, and crop frames prior to annotation ingestion. • Transformed annotation logs using NumPy/Pandas into analysis-ready DataFrames for QA reporting. • Reduced annotation error rates by 22% across two project cycles via QA collaboration. • Produced schema-compliant annotation batches with consistent CSV/JSON metadata.

2022 - 2022

AI Data Contributor (Handshake AI)

Text

Annotated NLP datasets for intent classification, named entity recognition (NER), and sentiment labeling to train downstream models. Converted raw collected data into ML-ready JSON/CSV using Python automation to eliminate repetitive formatting tasks. Enforced consistent field naming, null-value handling, and version-controlled JSON schema files to support reliable handoff to ML engineers. • Created structured intent/NER/sentiment labels for NLP model training. • Achieved quality scores above platform benchmarks while meeting daily throughput targets across multiple workstreams. • Reviewed TensorFlow/PyTorch model input specifications to ensure correctly shaped annotation outputs. • Contributed to a parallel image classification/tagging project for basic computer-vision labeling exposure.

2021 - 2022

Education

Work History