Amulya Tiwari

Key Skills

Software

Labelbox

LabelImg

Other

Top Subject Matter

AI-generated response evaluation

Computer vision/image annotation

Medical imaging/segmentation

Top Data Types

Text

Image

Top Task Types

Fine-tuning

Evaluation/Rating

Function Calling

Prompt + Response Writing (SFT)

Classification

Segmentation

Freelancer Overview

Prompt Engineer / Data Analyst (freelance) at Scale AI. Core strengths include Internal, Proprietary Tooling, and LabelImg. AI-training focus includes data types such as Text, Image, and Medical and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Prompt engineer/AI labeller

LabelboxTextFine TuningEvaluation Rating

Designed YAML-based benchmarks to evaluate LLM reasoning under implicit constraints, conflicting user intents, and real-world workflow dependencies. Built simulated environments with explicit world state, action schemas, and execution rules to objectively test decision sequencing and outcome correctness. Developed and audited evaluation rubrics to eliminate hidden failure modes and reliably differentiate model performance (pass/fail) without unfair ambiguity.

Designed YAML-based benchmarks to evaluate LLM reasoning under implicit constraints, conflicting user intents, and real-world workflow dependencies. Built simulated environments with explicit world state, action schemas, and execution rules to objectively test decision sequencing and outcome correctness. Developed and audited evaluation rubrics to eliminate hidden failure modes and reliably differentiate model performance (pass/fail) without unfair ambiguity.

2025 - Present

Machine Learning Intern at IIT-Indore

OtherSegmentation

I participated in a medical image segmentation project at IIT-Indore working with multi-modal MRI and CT scans. My role involved preparing, segmenting, and analyzing over 1,000 medical images to support model training. I improved segmentation accuracy by applying EDA techniques and validating results through visualization. • Labeled and segmented medical images for use in model training. • Utilized NumPy, pandas, OpenCV, and matplotlib for workflow automation and quality control. • Applied domain knowledge to ensure accurate region segmentation in clinical imagery. • Supported team goals in performance benchmarking and reporting.

I participated in a medical image segmentation project at IIT-Indore working with multi-modal MRI and CT scans. My role involved preparing, segmenting, and analyzing over 1,000 medical images to support model training. I improved segmentation accuracy by applying EDA techniques and validating results through visualization. • Labeled and segmented medical images for use in model training. • Utilized NumPy, pandas, OpenCV, and matplotlib for workflow automation and quality control. • Applied domain knowledge to ensure accurate region segmentation in clinical imagery. • Supported team goals in performance benchmarking and reporting.

2022 - 2022

Data Analyst Intern at Sciffer Analytics Private Limited

LabelimgImageClassification

I collected and annotated over 5,000 images to train a computer vision model for Sciffer Analytics. Labeling tasks included classifying images to optimize a YOLO-based object detection workflow. I improved data labeling efficiency using LabelImg and contributed to higher model performance. • Managed large-scale image annotation processes for object detection. • Used LabelImg to streamline labeling tasks and maintain data accuracy. • Contributed to annotation guidelines optimizing classification speed and quality. • Supported model validation with labeled datasets in iterative development.

I collected and annotated over 5,000 images to train a computer vision model for Sciffer Analytics. Labeling tasks included classifying images to optimize a YOLO-based object detection workflow. I improved data labeling efficiency using LabelImg and contributed to higher model performance. • Managed large-scale image annotation processes for object detection. • Used LabelImg to streamline labeling tasks and maintain data accuracy. • Contributed to annotation guidelines optimizing classification speed and quality. • Supported model validation with labeled datasets in iterative development.

2021 - 2022

Education

Work History