Sebastine Nnanemere

Key Skills

Software

Label Studio

AWS SageMaker

CrowdSource

Top Subject Matter

Cybersecurity Domain Expertise

Security Alerts

Threat Intelligence

Top Data Types

Text

Image

Document

Top Task Types

Classification

RLHF

Bounding Box

Segmentation

Object Detection

Freelancer Overview

AI Data Labelling Specialist – Security Domain. Brings 10+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Label Studio. AI-training focus includes data types such as Text and labeling workflows, including Classification and RLHF.

Labeling Experience

AI Data Labelling Specialist – Security Domain

Label StudioTextClassification

Led application of structured classification frameworks to ambiguous security threat and alert data for AI annotation projects. Regularly reviewed and triaged large volumes of security-focused events, assigning risk severity and policy-based labels to support model training. Automated compliance evidence and documentation, mirroring annotation pipelines and data labeling workflows. • Maintained strict adherence to annotation guidelines for security content • Evaluated ambiguous or adversarial technical inputs for label consistency • Produced audit-ready, structured technical outputs under defined rubrics • Applied categorical rules and standardized tagging across event streams

Led application of structured classification frameworks to ambiguous security threat and alert data for AI annotation projects. Regularly reviewed and triaged large volumes of security-focused events, assigning risk severity and policy-based labels to support model training. Automated compliance evidence and documentation, mirroring annotation pipelines and data labeling workflows. • Maintained strict adherence to annotation guidelines for security content • Evaluated ambiguous or adversarial technical inputs for label consistency • Produced audit-ready, structured technical outputs under defined rubrics • Applied categorical rules and standardized tagging across event streams

2024 - Present

RLHF Response Evaluator & Annotation Mentor

Label StudioTextRLHF

Evaluated and rated the quality, relevance, and factuality of technical content in the cybersecurity domain for reinforcement learning from human feedback (RLHF). Applied expert standards to assess responses, mentored practitioners for annotation consistency, and structured content for AI model preference training. Designed and reviewed labs ensuring alignment with evaluation rubrics and annotation best practices. • Conducted RLHF response ranking and feedback for AI system tuning • Translated complex domain knowledge into accessible, labeled formats • Coached junior annotators for inter-rater reliability and calibration • Applied clear grading criteria to technical content and scenario labs

Evaluated and rated the quality, relevance, and factuality of technical content in the cybersecurity domain for reinforcement learning from human feedback (RLHF). Applied expert standards to assess responses, mentored practitioners for annotation consistency, and structured content for AI model preference training. Designed and reviewed labs ensuring alignment with evaluation rubrics and annotation best practices. • Conducted RLHF response ranking and feedback for AI system tuning • Translated complex domain knowledge into accessible, labeled formats • Coached junior annotators for inter-rater reliability and calibration • Applied clear grading criteria to technical content and scenario labs

2023 - Present

Cloud Security & Compliance Data Labeling Specialist

Label StudioTextClassification

Executed high-volume classification, labelling, and documentation of cloud security and compliance findings for AI annotation and training. Used rule-based judgments to categorize alerts by type and severity, supporting the creation of structured, audit-ready datasets. Authored evidence packages that translated technical security findings into labelled, standards-compliant outputs. • Processed daily security alerts for entity and incident classification • Performed structured risk assessment labeling on varied technical data • Created documentation formatted for AI model consumption and labeling • Ensured accuracy and reproducibility of tagged compliance evidence

Executed high-volume classification, labelling, and documentation of cloud security and compliance findings for AI annotation and training. Used rule-based judgments to categorize alerts by type and severity, supporting the creation of structured, audit-ready datasets. Authored evidence packages that translated technical security findings into labelled, standards-compliant outputs. • Processed daily security alerts for entity and incident classification • Performed structured risk assessment labeling on varied technical data • Created documentation formatted for AI model consumption and labeling • Ensured accuracy and reproducibility of tagged compliance evidence

2023 - 2024

Security Annotation & Classification Expert

Label StudioTextClassification

Performed classification, annotation, and structured reporting on security events and vulnerability findings in support of AI/ML model training and evaluation. Developed labeling schemas for adversarial vs. benign activity, applied severity grades, and produced action-driven summaries. Maintained a consistent, high-accuracy throughput in labeling workloads over several years. • Applied incident labeling to logs and event streams for SIEM • Assessed and annotated vulnerabilities for severity, exploitability, and remediation • Authored high-fidelity, labeled documentation for machine learning pipelines • Built and maintained pattern-recognition rules for adversarial labeling

Performed classification, annotation, and structured reporting on security events and vulnerability findings in support of AI/ML model training and evaluation. Developed labeling schemas for adversarial vs. benign activity, applied severity grades, and produced action-driven summaries. Maintained a consistent, high-accuracy throughput in labeling workloads over several years. • Applied incident labeling to logs and event streams for SIEM • Assessed and annotated vulnerabilities for severity, exploitability, and remediation • Authored high-fidelity, labeled documentation for machine learning pipelines • Built and maintained pattern-recognition rules for adversarial labeling

2020 - 2023

Education

Work History