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Abhishek Singh Rathore

@abhisheksinghrathore

I improve production LLM reliability through evaluation, prompt engineering, hallucination detection, and structured feedback.

India

Message

What I'm looking for

I'm looking to build production-ready AI solutions where I can improve LLM accuracy, reliability, contextual grounding, and secure development quality practices.

At Turing, I evaluate LLM reasoning quality, factual accuracy, and response coherence for production-grade AI systems. I use static analysis to validate logical correctness, code quality, and policy compliance.

I've contributed structured feedback, dataset annotation, and quality benchmarking to AI/ML training workflows. I also optimize prompts, support RAG validation, and improve contextual grounding to reduce hallucinations and strengthen response reliability.

I built DocuMind, an automated document processing system using Python, SQL, and REST APIs. Its ingestion, validation, metadata storage, and dashboard workflows reduced manual processing effort by 60%.

I've also built a real-time smart parking booking system with React, Node.js, and MongoDB for 50+ simulated users, and earned 4th rank for Best Innovation in Utility Tech at an inter-college hackathon.

Experience

Work history, roles, and key accomplishments

Current

LLM Engineer

Current

Turing

Feb 2026 - Present (6 months)

Performed comprehensive LLM checks to evaluate reasoning quality, factual accuracy, and response coherence. Conducted static analysis and optimized prompt engineering to reduce hallucinations and improve model performance.

LLM Evaluation Prompt Engineering Static Analysis Hallucination Detection RAG Validation Model Training