LLM Research & Prompt Engineering Engineer

Ashley Everette

AI / ML Engineer · LLM Research, Prompt Engineering & Data Quality

Kannapolis, NC 301-***-**** **************@*******.*** LinkedIn Education

Williamsburg, KY Expected Graduation: May 2028

University of Cumberland Ph.D. in Information Technology (AI concentration) St. Davids, PA Dec 2023

Eastern University M.S. in Data Science

Adelphi, MD Dec 2018

University of Maryland Global Campus M.S. in Management Information Systems & Business Administration Greensboro, NC Dec 2011

North Carolina A&T State University B.S. in Electronics & Information Technology Technical Skills

Tools & Platforms: Cursor, Claude, OpenClaw, ChatGPT, Perplexity, Gemini, Replit, Lovable Models & Techniques: Large Language Models (LLMs), generative AI, agentic workflows, retrieval augmented generation (RAG), prompt engineering

Annotation & Evaluation: Data annotation (text, audio, video, images), labeling tools, QA calibration, inter rater reliability

Research & Analysis: Experiment design, benchmarking, error analysis, model evaluation, written research summaries Data & Productivity: Excel, Word, PowerPoint; data systems & analytics; Google Cloud Platform Experience

AI Engineer Apr 2026 – Present

Krybe Remote

Research and prototype LLM-based educational features; designed experiments to compare model behavior across Cursor, Claude, OpenClaw and other LLMs to identify best-fit approaches for product use cases. Built and optimized prompt engineering frameworks and agentic workflows; integrated retrieval-augmented generation

(RAG) to improve contextual recall and response accuracy. Conducted performance benchmarking and QA testing for generative AI components; authored test cases, validated model outputs, and contributed to code reviews to support production readiness. Collaborated with engineers and product stakeholders to translate research findings into technical requirements and experimental roadmaps.

AI Trust & Safety Evaluation Consultant Apr 2026 – June 2026 Linkedin Remote

Evaluated adversarial AI responses for safety, policy compliance and content relevance across hate, sexual content, violence, harassment and self-harm categories using structured rubrics. Applied severity ratings and produced concise written justifications to improve dataset quality and inter rater reliability for model safety training.

Participated in QA calibration and incorporated feedback to enhance annotation consistency and dataset reliability for downstream model evaluation.

Data Annotator / QA Consultant Jan 2026 – Apr 2026 Experis Remote

Completed rigorous training in labeling and QA standards; classified content sensitivity and reviewed legal documents to support data integrity and compliance.

Applied detailed quality-control measures to ensure proper handling of sensitive information and supported process improvements.

English Language Model Specialist Nov 2025 – Feb 2026 iMerit Scholars Remote

Generated and validated high-quality visual datasets and performed frame-by-frame video labeling; corrected algorithm-generated bounding boxes to improve detection and tracking accuracy. Documented annotation edge cases and provided structured feedback to update guidelines and streamline labeling workflows.

English Language Audio Model Trainer Nov 2025 – Jan 2026 Mercor Remote

Created clear audio recordings and annotated multimodal datasets to support speech+vision model training; enforced linguistic standards and dataset quality controls. Collaborated with researchers and QA teams to identify dataset issues and implement quality improvements. Code Rater Feb 2024 – Mar 2024· Remote

Outlier

Designed diverse coding problems and evaluated AI-generated code snippets for accuracy, performance, and clarity to help train next generation code models.

Authored clear code examples and explanations used to improve model-response quality. Language Model Generalist Rater Jan 2023 – Nov 2025 GlobalLogic Remote

Annotated and evaluated LLM outputs and conversational AI responses for a Fortune Tech 10 client to improve accuracy, safety, and user experience.

Performed structured assessments using evaluation rubrics; assigned severity and relevance ratings and provided written rationales to guide model training and policy updates. Identified and documented bias and safety edge cases; collaborated with ML engineers and researchers to prioritize fixes and refine training data

Participated in QA calibration sessions to raise inter-rater reliability and improve annotation consistency across large- scale datasets.

Synthesized feedback from user-facing issues and monitoring signals to propose iterative model improvements and annotation guideline changes.

Achievements

Business Analytics Certification — Simplilearn (2021).

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