Neeraj N.

Key Skills

Software

No software listed

Top Subject Matter

Web-based AI content generation (LLM integration)

LLM fine-tuning and translation/localization (Hinglish)

Top Data Types

Text

Image

Top Task Types

Prompt + Response Writing (SFT)

Fine-tuning

Freelancer Overview

Web Development and Machine Learning Intern — Varenyam Yoga. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include AWS and Docker. Education includes Master of Computer Application, Bennett University (2027) and Bachelor of Computer Application, Amrapali University (2025). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Prompt + Response Writing (SFT) and Fine-tuning.

Labeling Experience

Web Development and Machine Learning Intern — Varenyam Yoga

TextPrompt + Response Writing (SFT)

Built and integrated AI-assisted text generation features by connecting GPT-4 and Gemini APIs into production REST endpoints. Focused on prompt engineering to drive content generation that reduced manual client work. Deployed the AI functionality as part of real-time, ML-powered features serving high request volumes. • Implemented GPT-4/Gemini API integration for client content workflows • Designed prompts for consistent, task-specific outputs • Supported deployment of AI-assisted features within FastAPI services • Improved user-facing outcomes via measurable adoption and engagement impact

2025 - 2025

Project — Cross-Language Meme and Slang Translator (Fine-tuning FLAN-T5)

Fine-tuning

Fine-tuned an FLAN-T5 model on a custom Hinglish and code-mixed dataset to improve translation quality. Used targeted prompt engineering to achieve higher evaluation scores versus a zero-shot baseline. Prepared the training workflow for practical reuse via containerization and one-command deployment. • Performed supervised fine-tuning of FLAN-T5 on domain-specific data • Applied prompt engineering strategies to improve output quality • Containerized the pipeline for reproducible cloud execution • Evaluated improvements using SacreBLEU metrics

2025

Education

Work History