Hilcon Alexis Sumatra

Key Skills

Software

Appen

Labelbox

Lionbridge

OneForma

Remotasks

Scale AI

Telus

V7 Labs

Other

Google Cloud Vertex AI

Top Subject Matter

UI/UX - Mobile and Web Applications

Biology - Large Language Models

UI Data Annotation Quality Assurance

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Cuboid

Prompt + Response Writing (SFT)

Question Answering

Text Generation

Classification

Freelancer Overview

UX Labeller. Core strengths include Labelbox, Fine-Tune Studio (FTS), and Other. AI-training focus includes data types such as Image and Text and labeling workflows including Classification and Prompt + Response Writing (SFT).

Labeling Experience

UX Labeller

LabelboxImageClassification

As a UX Labeller at Mobbin Pte. Ltd, I annotated over 1,000 UI elements to support training of visual query AI systems. I followed detailed labeling guidelines and validation protocols, achieving high annotation accuracy. I also contributed to refining annotation standards to enhance downstream model performance. • Labeled mobile and web UI elements using standardized workflows • Collaborated with team members to resolve ambiguous annotation cases • Improved dataset consistency and reduced rework cycles • Enhanced labeling efficiency and quality control measures.

As a UX Labeller at Mobbin Pte. Ltd, I annotated over 1,000 UI elements to support training of visual query AI systems. I followed detailed labeling guidelines and validation protocols, achieving high annotation accuracy. I also contributed to refining annotation standards to enhance downstream model performance. • Labeled mobile and web UI elements using standardized workflows • Collaborated with team members to resolve ambiguous annotation cases • Improved dataset consistency and reduced rework cycles • Enhanced labeling efficiency and quality control measures.

2025 - Present

Annotation Quality Optimization Project

OtherImageClassification

In the Annotation Quality Optimization Project, I designed annotation guidelines to improve labeling consistency for UI datasets. My efforts reduced annotation errors and addressed edge-case inconsistencies. I implemented structured validation rules to ensure quality in AI training datasets. • Developed workflow enhancements for dataset usability • Improved consistency and reduced ambiguities in labeling • Created guidelines tailored for UI annotation • Focused on quality assurance in dataset preparation.

In the Annotation Quality Optimization Project, I designed annotation guidelines to improve labeling consistency for UI datasets. My efforts reduced annotation errors and addressed edge-case inconsistencies. I implemented structured validation rules to ensure quality in AI training datasets. • Developed workflow enhancements for dataset usability • Improved consistency and reduced ambiguities in labeling • Created guidelines tailored for UI annotation • Focused on quality assurance in dataset preparation.

2025 - 2025

AI Prompt-Response Evaluator

TextPrompt Response Writing SFT

As an AI Prompt-Response Evaluator at TELUS International AI, I developed and evaluated domain-specific prompts and responses for LLM training. My work focused on improving response structure, tone consistency, and relevance to bio-domain tasks. I conducted iterative quality reviews to align with training objectives. • Created over 500 prompts and responses for biology-focused LLMs • Increased user engagement and LLM output quality • Used Fine-Tune Studio for prompt and response engineering • Performed iterative feedback and refinement of LLM outputs.

As an AI Prompt-Response Evaluator at TELUS International AI, I developed and evaluated domain-specific prompts and responses for LLM training. My work focused on improving response structure, tone consistency, and relevance to bio-domain tasks. I conducted iterative quality reviews to align with training objectives. • Created over 500 prompts and responses for biology-focused LLMs • Increased user engagement and LLM output quality • Used Fine-Tune Studio for prompt and response engineering • Performed iterative feedback and refinement of LLM outputs.

2024 - 2025

Google AI Essentials – Applied Prompt Engineering

Google Cloud Vertex AITextPrompt Response Writing SFT

As part of the Google AI Essentials certification, I applied prompt engineering to improve AI-generated text outputs. I utilized generative AI tools for creating and refining content and workflows. The work included identifying bias and ensuring responsible AI usage. • Practiced advanced prompt engineering techniques for AI outputs • Used Google generative AI tools for content creation • Ensured workflow efficiency through prompt iteration • Implemented bias mitigation strategies in AI responses.

As part of the Google AI Essentials certification, I applied prompt engineering to improve AI-generated text outputs. I utilized generative AI tools for creating and refining content and workflows. The work included identifying bias and ensuring responsible AI usage. • Practiced advanced prompt engineering techniques for AI outputs • Used Google generative AI tools for content creation • Ensured workflow efficiency through prompt iteration • Implemented bias mitigation strategies in AI responses.

2024 - 2024

Prompt Engineering Optimization Project

OtherTextPrompt Response Writing SFT

During the Prompt Engineering Optimization Project, I developed structured prompt frameworks for improved LLM response accuracy and coherence. This included iterative testing and refinement of outputs. The project aimed to optimize response relevance and tone consistency for AI applications. • Built and tested prompt structures for LLMs • Focused on coherence and correctness of AI-generated outputs • Refined prompt frameworks through iterative feedback • Enhanced tone and domain accuracy in model responses.

During the Prompt Engineering Optimization Project, I developed structured prompt frameworks for improved LLM response accuracy and coherence. This included iterative testing and refinement of outputs. The project aimed to optimize response relevance and tone consistency for AI applications. • Built and tested prompt structures for LLMs • Focused on coherence and correctness of AI-generated outputs • Refined prompt frameworks through iterative feedback • Enhanced tone and domain accuracy in model responses.

2024 - 2024

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Education

Work History