Daniel O.

Key Skills

Software

Roboflow

Remotasks

Don't disclose

Top Subject Matter

Computer Science & Software Engineering

Artificial Intelligence & Machine Learning

Data Science & Spatial Analytics (GIS)

Top Data Types

Text

Geospatial Tiled Imagery

Image

Top Task Types

Bounding Box

Polygon

Segmentation

Classification

Entity (NER) Classification

Polyline

Point/Key Point

Object Detection

Text Generation

Text Summarization

RLHF

Fine-tuning

Transcription

Evaluation/Rating

Computer Programming/Coding

Data Collection

Conversation Design

AI Trainer Overview

As a Software Engineer, Technical Lead, and University Lecturer with over 5 years of experience, I bring deep subject-matter expertise to AI data labeling, code evaluation, and model training workflows. My technical background spans full-stack software development (Python, TypeScript, Java, C++, C, SQL) and production system architecture, alongside academic instruction in Data Structures & Algorithms, Database Design, and Machine Learning. I have hands-on experience building Retrieval-Augmented Generation (RAG) ecosystems, vector database integrations (Pinecone), and domain-specific NLP pipelines, making me uniquely qualified for high-complexity AI training tasks. What sets me apart is my ability to provide rigorous, step-by-step reasoning (Chain-of-Thought) and clear pedagogical feedback required for Reinforcement Learning from Human Feedback (RLHF). Whether evaluating AI-generated code for syntax, logic, and security vulnerabilities, executing code-red-teaming, or annotating complex technical datasets, my dual background in industry engineering and university-level computer science instruction ensures accurate, high-quality human oversight for advanced AI models.

Labeling Experience

Spatial & Geospatial Dataset Curation (East African Urban Density)

Geospatial Tiled ImageryBounding Box

Spatial & Geospatial Dataset Annotation Curated, processed, and annotated over 1,500 high-resolution GeoTIFF tile files from the Open Buildings 2.5D dataset across five major East African metropolitan areas. Performed spatial attribute tagging, urban density classification, and temporal 3D height mapping. Cleared spatial anomalies, validated building boundary footprints, and created structured metadata layers using ArcGIS Pro and Python. Transformed massive geospatial datasets into clean, actionable analytical layers for spatial modeling and AI analysis.

2024 - 2025

RAG Knowledge Base Curation & Vectorization ("Mother" AI Ecosystem)

DocumentSegmentation

RAG Knowledge Base Curation & Vectorization ("Mother" AI Ecosystem) Built end-to-end data ingestion pipelines to clean, chunk, and annotate large-scale unstructured institutional documents for a Retrieval-Augmented Generation (RAG) system. Vectorized high-dimensional text data into Pinecone vector databases paired with PostgreSQL metadata stores. Conducted manual verification of retrieval accuracy across natural language query-response pairs, evaluating relevance scores and eliminating model hallucinations. Structured reference ground-truth answer sets to ensure context-aware, highly accurate LLM responses for enterprise document querying.

2024 - 2025

Code & Technical Reasoning Evaluation (Computer Science & Software Engineering)

Computer Code ProgrammingRLHF

Code & Technical Reasoning Evaluation Evaluated and annotated complex code snippets, algorithmic logic, and database query executions for advanced software engineering workflows and university curriculum support. Verified code written in Python, TypeScript, Java, C++, and SQL against strict correctness, execution efficiency, memory management, and security standards (OWASP guidelines). Crafted detailed, step-by-step Chain-of-Thought (CoT) reasoning breakdowns explaining logical bugs, syntax errors, and edge-case failures. Formatted clear target responses to train models on generating human-readable, production-grade code explanations.

2023 - 2024

Unstructured Script Text Mining & Sentiment Labeling (Star Wars NLP Study)

TextEntity (NER) Classification

Unstructured Script Text Mining & Sentiment Labeling Processed, structured, and labeled over 10,000 lines of raw, unstructured movie script dialogue for advanced NLP sentiment analysis and character interaction mapping. Cleaned and tokenized raw text data, categorizing dialogue snippets by emotional valence (positive, negative, neutral) and narrative context. Created annotated character interaction matrices to map network density and sentiment shifts across character arcs. Validated classification accuracy using automated Python NLP toolkits alongside manual dataset spot-checks.

2022 - 2022

Education

Work History