Akash

Key Skills

Software

Scale AI

Top Subject Matter

AI model training and evaluation (RLHF/SFT) with contributor quality assurance

Engineering infrastructure supporting AI training/evaluation quality

Top Data Types

Computer Code Programming

Text

Document

Top Task Types

RLHF

Prompt + Response Writing (SFT)

Function Calling

Data Collection

Computer Programming/Coding

Text Generation

Classification

Evaluation/Rating

Question Answering

Freelancer Overview

Tata 1mg Healthcare Solutions Pvt. Ltd — SDE work supporting evaluation/testing pipelines (07/2022 - 09/2024). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, Internal, and Proprietary Tooling. Education includes Bachelor of Technology, Indian Institute of Technology, Kanpur (2022) and Senior Secondary (AISSCE), Subhash Public School, Giridih (2017). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including RLHF, Computer Programming, and Coding.

Labeling Experience

Scale AI — Contributor onboarding support and RLHF training/rating review (11/2024 - Present)

Scale AIRLHF

Performed RLHF training for AI models by providing ratings across multiple evaluation dimensions such as accuracy, instruction following, and presentation. Supervised contributor onboarding and supported their understanding of project requirements to maintain consistent labeling quality. Audited submitted work to ensure ratings and outputs matched client expectations. • Provided model ratings across specified rubric dimensions • Facilitated communication and resolved contributor challenges • Quality assurance audits of contributor tasks for compliance • Participated in prompt and rubric design to control prompt complexity

2024 - Present

Tata 1mg Healthcare Solutions Pvt. Ltd — SDE work supporting evaluation/testing pipelines (07/2022 - 09/2024)

Supported AI/data labeling-oriented work indirectly through building automated testing and evaluation pipelines that improve model and code output reliability. Improved code coverage and integrated automated checks to ensure consistent quality for downstream AI training workflows. Implemented security scanning to reduce vulnerabilities that can affect model development and evaluation environments. • Integrated automated testing with pytest and coverage • Improved microservice test coverage from 70% to 98% • Added code-quality CI checks via linting and formatting pipelines • Implemented detect-secrets scanning for sensitive information

2022 - 2024

Education

Work History