Faizal

Key Skills

Software

AWS SageMaker

Google Cloud Vertex AI

Label Studio

Remotasks

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Image

Medical Dicom

Text

Video

Top Label Types

Bounding Box

Classification

Computer Programming Coding

Emotion Recognition

Entity Ner Classification

Evaluation Rating

Fine Tuning

Function Calling

Object Detection

Prompt Response Writing SFT

Question Answering

RLHF

Segmentation

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am an AI and data science professional with hands-on experience in NLP, data annotation, and building training data pipelines for enterprise applications. My background includes developing domain-specific NL2SQL systems, architecting advanced NLP pipelines for resume parsing, and creating multi-agent RAG-based chatbots—all of which required meticulous data labeling, prompt engineering, and model fine-tuning. I have worked with tools such as Python, HuggingFace Transformers, spaCy, LangChain, and vector databases like FAISS and Pinecone, and have experience optimizing data workflows for LLM-powered analytics and conversational AI. My projects span areas including natural language processing, generative AI, and healthcare, where I have designed intelligent systems that rely on high-quality, annotated datasets to deliver robust AI solutions.

Labeling Experience

NER for Medical and Pharmaceutical Domain

Label StudioMedical DicomEntity Ner Classification

Worked on NER annotation for pharmaceutical and medical NLP datasets using Label Studio, ensuring high-quality labeled data for training and evaluation of AI models. Annotated complex clinical and biomedical entities such as Drug Names, Dosage, Strength, Frequency, Route of Administration, Adverse Drug Reactions (ADR), Symptoms, Diseases, Lab Tests, Procedures, and Medical Devices. Followed strict annotation guidelines, maintained consistency across documents, handled ambiguous medical terminology through context-based decisions, and supported model improvement by reducing noise in training data through accurate span-level tagging and quality validation.

Worked on NER annotation for pharmaceutical and medical NLP datasets using Label Studio, ensuring high-quality labeled data for training and evaluation of AI models. Annotated complex clinical and biomedical entities such as Drug Names, Dosage, Strength, Frequency, Route of Administration, Adverse Drug Reactions (ADR), Symptoms, Diseases, Lab Tests, Procedures, and Medical Devices. Followed strict annotation guidelines, maintained consistency across documents, handled ambiguous medical terminology through context-based decisions, and supported model improvement by reducing noise in training data through accurate span-level tagging and quality validation.

2024 - 2025

Education

Work History