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Segni Tsega

Applied LLM Engineer (Evaluation & Alignment Systems)

Ethiopia flagAddis Ababa, Ethiopia
$25.00/hrExpertAws SagemakerDatasaurDoccano

Key Skills

Software

AWS SageMakerAWS SageMaker
DatasaurDatasaur
DoccanoDoccano
HastyHasty

Top Subject Matter

Natural Language Processing (NLP)
LLM Evaluation & Alignment
LLM Dataset Curation

Top Data Types

TextText
ImageImage

Top Task Types

Fine-tuningFine-tuning
DiagnosisDiagnosis

Freelancer Overview

Applied LLM Engineer (Evaluation & Alignment Systems). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, Adama Science and Technology University (2022). AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and Fine-tuning.

ExpertEnglish

Labeling Experience

Applied LLM Engineer (Evaluation & Alignment Systems)

Text
As an Applied LLM Engineer, I designed and executed Python-based evaluation frameworks to benchmark large language models across various NLP tasks. My work involved developing structured prompt engineering and testing workflows, iteratively refining evaluation criteria, and improving the quality of training data for fine tuning. The experience enhanced overall response consistency and alignment of LLMs. • Benchmarked LLM performance across accuracy, reasoning, and reliability. • Created ideal response definitions for downstream model fine-tuning. • Implemented systematic prompt and evaluation workflow designs. • Enhanced alignment via rigorous analysis and data quality checks.

As an Applied LLM Engineer, I designed and executed Python-based evaluation frameworks to benchmark large language models across various NLP tasks. My work involved developing structured prompt engineering and testing workflows, iteratively refining evaluation criteria, and improving the quality of training data for fine tuning. The experience enhanced overall response consistency and alignment of LLMs. • Benchmarked LLM performance across accuracy, reasoning, and reliability. • Created ideal response definitions for downstream model fine-tuning. • Implemented systematic prompt and evaluation workflow designs. • Enhanced alignment via rigorous analysis and data quality checks.

2022 - Present

Fine Tuning Dataset & LLM Benchmarking Project

TextFine Tuning
I led the design and curation of fine tuning datasets by methodically benchmarking different LLM architectures over a variety of NLP tasks. I developed evaluation scripts to compare outputs, diagnose performance gaps, and implement data-driven refinement strategies. My efforts brought improvements in model alignment, response consistency, and downstream task accuracy. • Curated high-quality datasets for LLM fine-tuning. • Used iterative dataset validation and quality control processes. • Identified and resolved model output weaknesses through benchmarking. • Supported structured evaluation for optimal model performance.

I led the design and curation of fine tuning datasets by methodically benchmarking different LLM architectures over a variety of NLP tasks. I developed evaluation scripts to compare outputs, diagnose performance gaps, and implement data-driven refinement strategies. My efforts brought improvements in model alignment, response consistency, and downstream task accuracy. • Curated high-quality datasets for LLM fine-tuning. • Used iterative dataset validation and quality control processes. • Identified and resolved model output weaknesses through benchmarking. • Supported structured evaluation for optimal model performance.

2025 - 2025

AI-Powered Crop Disease Diagnosis App - Smart Gebere

ImageDiagnosis
As part of developing the AI-powered Smart Gebere crop disease diagnosis app, I integrated image classification models that required validated labeled image datasets. The process included designing end-to-end image preprocessing and inference pipelines under varied quality and field conditions. My contribution focused on ensuring robust model predictions and clear output interpretations for non-technical end users. • Applied quality control to agricultural image datasets. • Managed pipelines from image capture to model inference. • Optimized data handling for unreliable field imaging. • Improved classification accuracy for crop disease diagnosis.

As part of developing the AI-powered Smart Gebere crop disease diagnosis app, I integrated image classification models that required validated labeled image datasets. The process included designing end-to-end image preprocessing and inference pipelines under varied quality and field conditions. My contribution focused on ensuring robust model predictions and clear output interpretations for non-technical end users. • Applied quality control to agricultural image datasets. • Managed pipelines from image capture to model inference. • Optimized data handling for unreliable field imaging. • Improved classification accuracy for crop disease diagnosis.

Not specified

Education

A

Adama Science and Technology University

Bachelor of Science, Software Engineering

Bachelor of Science
2018 - 2022

Work History

Y

Yigegnu Cloud Technologies

Applied LLM Engineer

Addis Ababa
2022 - Present
Y

Yigegnu Cloud Technologies

Software Engineer

Addis Ababa
2023 - 2025