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Saaki Nirula

Saaki Nirula

Business Tuning Analyst

USA flagdallas, texas, Usa
$30.00/hrExpertDataloopData Annotation TechDeep Systems

Key Skills

Software

DataloopDataloop
Data Annotation TechData Annotation Tech
Deep SystemsDeep Systems
EncordEncord
Figure EightFigure Eight
HumanaticHumanatic
LabelboxLabelbox
LightTagLightTag
MercorMercor
Mighty AIMighty AI
Scale AIScale AI
TelusTelus
V7 LabsV7 Labs

Top Subject Matter

Healthcare
Finance
Customer Support

Top Data Types

DocumentDocument
ImageImage
TextText

Top Task Types

ClassificationClassification
Point/Key PointPoint/Key Point
Text GenerationText Generation
Question AnsweringQuestion Answering
Text SummarizationText Summarization
Fine-tuningFine-tuning
TranscriptionTranscription
Evaluation/RatingEvaluation/Rating
Data CollectionData Collection
Function CallingFunction Calling
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
SegmentationSegmentation
Entity (NER) ClassificationEntity (NER) Classification

Freelancer Overview

Business Analyst / A.I. Prompt Engineer. Bringing 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Dual Bachelors of Science graduate from University of North Texas (2022).

ExpertEnglish

Labeling Experience

Medical Q&A AI Training and Annotation

Medical DicomFine Tuning
I worked on training AI models for Question Answering about Health by doing tasks such as annotating (providing a label to) and assessing medical queries and their answers. I labeled query types as pertinent to the user type (i.e., patient-style), identified the topicality of the query (i.e., symptom, treatment, medication), and evaluated the level of accuracy, clarity, and safety of the AI-produced responses. I worked to eliminate misinformation and to ensure that AI-produced responses adhered to responsible artificial intelligence standards, especially when addressing sensitive health issues. I rewrote or rephrased AI-produced responses to improve their ease of understanding for individuals without medical expertise, while maintaining the factual correctness of the responses. I have managed a large database of medical Q&A pairs and I have implemented a series of strict quality controls to verify guideline compliance, consistency, and extensively document any errors (i.e., inappropriate recommended treatment, omission of disclaimers, unsafe treatment suggestion).

I worked on training AI models for Question Answering about Health by doing tasks such as annotating (providing a label to) and assessing medical queries and their answers. I labeled query types as pertinent to the user type (i.e., patient-style), identified the topicality of the query (i.e., symptom, treatment, medication), and evaluated the level of accuracy, clarity, and safety of the AI-produced responses. I worked to eliminate misinformation and to ensure that AI-produced responses adhered to responsible artificial intelligence standards, especially when addressing sensitive health issues. I rewrote or rephrased AI-produced responses to improve their ease of understanding for individuals without medical expertise, while maintaining the factual correctness of the responses. I have managed a large database of medical Q&A pairs and I have implemented a series of strict quality controls to verify guideline compliance, consistency, and extensively document any errors (i.e., inappropriate recommended treatment, omission of disclaimers, unsafe treatment suggestion).

2024 - 2025

LLM Prompt Engineering and Output Evaluation

TextQuestion Answering
During my time working to improve and train large language models, I designed, tested, and refined prompts that span numerous different domains through the development of structured prompt templates. In doing so, I completed tasks such as generating structured prompt templates, evaluating accuracy, reasoning quality, and adherence to instructions for model responses, and ranking models' responses on their relevance and completeness. I also annotated a variety of datasets to be used for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), including the classification of responses as either; correct/incorrect, helpful/unhelpful, and unsafe/safe. I was able to maintain consistency throughout the annotation process by implementing strict guidelines when annotating and ensuring that inter-annotator agreement was as high as possible. The project consisted of thousands of separate prompt-response pairs for a variety of topic areas (i.e., finance, coding, general knowledge, etc.), with particular emphasis placed on improving model reasoning, reducing hallucination, and optimizing output clarity.

During my time working to improve and train large language models, I designed, tested, and refined prompts that span numerous different domains through the development of structured prompt templates. In doing so, I completed tasks such as generating structured prompt templates, evaluating accuracy, reasoning quality, and adherence to instructions for model responses, and ranking models' responses on their relevance and completeness. I also annotated a variety of datasets to be used for supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), including the classification of responses as either; correct/incorrect, helpful/unhelpful, and unsafe/safe. I was able to maintain consistency throughout the annotation process by implementing strict guidelines when annotating and ensuring that inter-annotator agreement was as high as possible. The project consisted of thousands of separate prompt-response pairs for a variety of topic areas (i.e., finance, coding, general knowledge, etc.), with particular emphasis placed on improving model reasoning, reducing hallucination, and optimizing output clarity.

2023 - 2024

Education

U

University of North Texas

Bachelor of Science, Business Analytics and Marketing

Bachelor of Science
2020 - 2022

Work History

T

TTA Systems

Business Tuning Analyst

Buffalo, NY
2024 - 2025