Murat S.

Key Skills

Software

Other

Don't disclose

Top Subject Matter

Nlp Domain Expertise

linguistic auditing

and AI output evaluation

Top Data Types

Text

Audio

Document

Top Task Types

Text Generation

Freelancer Overview

Advanced NLP Intensive Training (Certified NLP Master Practitioner). Brings 34+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Other and Don't disclose. Education includes Master of Laws, Adilet Higher School of Law (2002) and Bachelor of Laws, Kainar University (2001). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Text Generation.

Labeling Experience

Generative AI workflows for AI training/testing/validation (through freelance practice)

Don't discloseTextText Generation

Built and utilized modern Generative AI workflows (LLMs, prompt engineering, and autonomous data agents) for training, testing, and validation of LLM outputs. Conducted structured linguistic analysis workflows intended to support RLHF-style evaluation and refinement of model responses. Applied neuro-linguistic programming techniques to extract linguistic structure used to validate and guide AI performance. • Used prompt engineering and evaluation loops to generate and refine model outputs. • Performed linguistic pattern dissection to support validation and testing workflows. • Applied meta-modeling and presupposition extraction for meaning-structure checks. • Used AI stress-testing and agent validation to improve reliability of outputs.

2009 - Present

Advanced NLP Intensive Training (Certified NLP Master Practitioner)

OtherText

Provided training and applied structured linguistic auditing techniques to calibrate and validate language outputs for precision, tone, and alignment. Performed deep textual analysis and fact-checking workflows that support quality evaluation of AI-generated or processed text. Documented and stress-tested autonomous AI agent behaviors through rigorous review cycles. • Structural linguistic meta-modeling and presupposition extraction to assess meaning consistency. • Detection of semantic manipulation and anomalies in text for downstream AI evaluation. • Prompt and response quality assessment aligned with training/validation goals. • Integration of calibration strategies to refine output reliability and consistency.

2004 - 2009

Education

Work History