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Fauzy Caesar Rochim

Fauzy Caesar Rochim

Tagging user questions with name entity recognition (NER) – Machine Learning Engineer at BRI

Indonesia flagJakarta, Indonesia
$30.00/hrEntry LevelGoogle Cloud Vertex AIAws SagemakerData Annotation Tech

Key Skills

Software

Google Cloud Vertex AIGoogle Cloud Vertex AI
AWS SageMakerAWS SageMaker
Data Annotation TechData Annotation Tech
Label StudioLabel Studio
Internal/Proprietary Tooling

Top Subject Matter

Healthcare / Medical
Finance / Banking Institution

Top Data Types

TextText
DocumentDocument

Top Task Types

Entity Ner Classification
Classification
Text Generation
RLHF
Text Summarization
Transcription
Data Collection
Prompt Response Writing SFT

Freelancer Overview

Tagging user questions with name entity recognition (NER) – Machine Learning Engineer at Alodokter. Brings 11+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Certificate Program, Purwadhika Startup & Coding School (2019) and Bachelor of Science, Universitas Gadjah Mada (2018). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification.

Entry LevelEnglishJapaneseIndonesian

Labeling Experience

Code Human Preferences Labeler

TextText Generation
For this project, I will select a codebase that is a git repository, and ask the model AI to perform a single task in that codebase. Our goal is to, over multiple turns, iterate on the model’s solution for that task until it reaches a “production-ready” state. This should be iterating on the model’s workflow with it to ensure it is working like a real engineer - meaning ensuring the model is reviewing the code it wrote, validating code against task requirements, committing regularly, etc.

For this project, I will select a codebase that is a git repository, and ask the model AI to perform a single task in that codebase. Our goal is to, over multiple turns, iterate on the model’s solution for that task until it reaches a “production-ready” state. This should be iterating on the model’s workflow with it to ensure it is working like a real engineer - meaning ensuring the model is reviewing the code it wrote, validating code against task requirements, committing regularly, etc.

2026 - Present

Tagging user questions with name entity recognition (NER) – Machine Learning Engineer at Alodokter

TextEntity Ner Classification
Tagging user questions with name entity recognition was a key task to automate the classification of medical queries. The project significantly reduced manual tagging requirements for the healthcare platform. Automated NER tagging enabled more efficient routing and improved quality of user experience. • Applied machine learning to annotate user-submitted text questions • Reduced manual data labeling efforts by approximately 80% • Focused on healthcare and medical subject matter • Part of a chatbot workflow to assist doctor consultations.

Tagging user questions with name entity recognition was a key task to automate the classification of medical queries. The project significantly reduced manual tagging requirements for the healthcare platform. Automated NER tagging enabled more efficient routing and improved quality of user experience. • Applied machine learning to annotate user-submitted text questions • Reduced manual data labeling efforts by approximately 80% • Focused on healthcare and medical subject matter • Part of a chatbot workflow to assist doctor consultations.

2019 - 2021

Education

P

Purwadhika Startup & Coding School

Certificate Program, Data Science

Certificate Program
2018 - 2019
U

Universitas Gadjah Mada

Bachelor of Science, Electronics Instrumentation

Bachelor of Science
2014 - 2018

Work History

B

BRI

Machine Learning Engineer

Jakarta
2021 - Present
A

Alodokter

Machine Learning Engineer

Jakarta
2019 - 2021