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D

David Nzuma

AI Model Evaluation and Prompt Engineering Tasks

KENYA flag
Nairobi, Kenya
$50.00/hrExpertAppenClickworkerAws Sagemaker

Key Skills

Software

AppenAppen
ClickworkerClickworker
AWS SageMakerAWS SageMaker
ArgillaArgilla
Axiom AI

Top Subject Matter

Large Language Models
Nlp Domain Expertise
Prompt Engineering

Top Data Types

TextText
Computer Code ProgrammingComputer Code Programming
Geospatial Tiled ImageryGeospatial Tiled Imagery

Top Task Types

Data Collection
Segmentation
Entity Ner Classification
Point Key Point
Classification
Polygon
Object Detection
Cuboid
Text Generation
Text Summarization
Question Answering
RLHF
Fine Tuning
Red Teaming
Transcription
Evaluation Rating
Computer Programming Coding
Function Calling
Prompt Response Writing SFT
Bounding Box
Polyline

Freelancer Overview

AI Model Evaluation and Prompt Engineering Tasks. Brings 13+ 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, KCA University. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Data Collection.

ExpertEnglish

Labeling Experience

AI Training Data Preparation and Prompt Optimization

TextData Collection
Assisted with the preparation and curation of datasets used for AI training workflows, ensuring high data quality and relevance. Contributed to the refinement of prompts and data samples for supervised fine-tuning. Produced technical documentation supporting dataset development for training and evaluation purposes. • Aggregated and cleaned text data for use in AI model fine-tuning • Designed and tested prompt variations for training consistency • Created reports assessing prompt and dataset effectiveness • Employed internal tools for data management and analysis

Assisted with the preparation and curation of datasets used for AI training workflows, ensuring high data quality and relevance. Contributed to the refinement of prompts and data samples for supervised fine-tuning. Produced technical documentation supporting dataset development for training and evaluation purposes. • Aggregated and cleaned text data for use in AI model fine-tuning • Designed and tested prompt variations for training consistency • Created reports assessing prompt and dataset effectiveness • Employed internal tools for data management and analysis

2021 - Present

AI Model Evaluation and Prompt Engineering Tasks

Text
Responsible for systematically evaluating the outputs of large language models for factual accuracy, reasoning quality, and instruction compliance. Designed and executed prompt engineering tasks to improve model responses within AI workflows. Conducted comprehensive model output validation as part of structured model training and assessment cycles. • Developed and maintained criteria for response evaluation • Collaborated with remote team members to review and rate LLM outputs • Documented analysis findings and contributed to dataset improvement • Utilized internal proprietary tools for structured evaluation

Responsible for systematically evaluating the outputs of large language models for factual accuracy, reasoning quality, and instruction compliance. Designed and executed prompt engineering tasks to improve model responses within AI workflows. Conducted comprehensive model output validation as part of structured model training and assessment cycles. • Developed and maintained criteria for response evaluation • Collaborated with remote team members to review and rate LLM outputs • Documented analysis findings and contributed to dataset improvement • Utilized internal proprietary tools for structured evaluation

2021 - Present

Model Output Evaluation and Error Analysis

Text
Evaluated large-scale machine learning model outputs for error analysis, performance, and improvement opportunities. Implemented structured processes to assess response quality and aligned results with client goals. Facilitated prompt iteration cycles to enhance dataset training. • Developed guidelines for model evaluation in data science workflows • Reported on evaluation findings to project stakeholders • Used internal evaluation tools integrated with analytics platforms • Coordinated with data scientists and software engineers on labeling-related tasks

Evaluated large-scale machine learning model outputs for error analysis, performance, and improvement opportunities. Implemented structured processes to assess response quality and aligned results with client goals. Facilitated prompt iteration cycles to enhance dataset training. • Developed guidelines for model evaluation in data science workflows • Reported on evaluation findings to project stakeholders • Used internal evaluation tools integrated with analytics platforms • Coordinated with data scientists and software engineers on labeling-related tasks

2018 - 2021

Automated Data Validation for ML Datasets

TextData Collection
Built and deployed automated data validation tools improving reliability of training datasets for machine learning. Verified training data quality and flagged anomalies for further labeling when necessary. Supported the preparation and transformation of datasets integrated into distributed ML pipelines. • Performed quality checks on collected and processed text data • Automated identification of data inconsistencies and errors • Documented validation processes to support reproducible data preparation • Collaborated with engineering teams on best practices for data validation

Built and deployed automated data validation tools improving reliability of training datasets for machine learning. Verified training data quality and flagged anomalies for further labeling when necessary. Supported the preparation and transformation of datasets integrated into distributed ML pipelines. • Performed quality checks on collected and processed text data • Automated identification of data inconsistencies and errors • Documented validation processes to support reproducible data preparation • Collaborated with engineering teams on best practices for data validation

2016 - 2018

Education

K

KCA University

Bachelor of Science, Software Development

Bachelor of Science
Not specified

Work History

A

Amazon

Senior Machine Learning Engineer

Nairobi
2021 - Present
M

Microsoft

Data Scientist

Nairobi
2018 - 2021