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deeplaksh yadav User

@deeplakshyadavuser

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ML Research Consultant at Epub Solutions Pvt. Ltd. building ML pipelines and intelligent insight generation.

India

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At Epub Solutions Pvt. Ltd., I develop, train, and evaluate machine learning, deep learning, NLP, RAG, and computer vision models. I also build end-to-end ML pipelines for preprocessing, feature engineering, evaluation, and insight generation.

I earned the Star Performer of the Month award within three months of joining Epub Solutions Pvt. Ltd., recognizing my technical contributions and project delivery.

At BlueStock Fintech, I developed a full-stack IPO dashboard with React, Node.js, Express.js, and MySQL. I implemented REST APIs, user authentication, and role-based access control.

I created AI training data and prompts for Outlier, supporting supervised fine-tuning and reinforcement learning from human feedback. In my projects, I built a customer support ticket classification and retrieval system and am developing SupportSpace, a mental wellness platform with generative AI features.

Experience

Work history, roles, and key accomplishments

Current

ML Research Consultant

Current

Epub Solutions Pvt. Ltd.

Mar 2026 - Present (7 months)

Developed, trained, and evaluated machine learning, deep learning, NLP, RAG, and computer vision models. Built end-to-end ML pipelines for data preprocessing, feature engineering, model evaluation, and intelligent insight generation.

Machine Learning Deep Learning NLP RAG Computer Vision data preprocessing Feature Engineering Model Evaluation Python

Software Development Engineer Intern

BlueStock Fintech

Jan 2026 - Mar 2026 (2 months)

Developed a full-stack IPO dashboard using React, Node.js, Express.js, and MySQL for financial data visualization and management. Implemented REST APIs, user authentication, and role-based access control (RBAC) to support secure application workflows.

React NodeJS ExpressJS MySQL REST APIs Authentication RBAC

Prompt Engineer (Freelance)

Outlier

Dec 2024 - Aug 2025 (8 months)

Created AI training data and prompts for supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Designed and evaluated prompts to improve large language model response quality, accuracy, alignment, and consistency.

Prompt Engineering SFT RLHF LLM AI Training Data