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Mohd Muneer Qureshi

@mohdmuneerqureshi

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I build multimodal RAG systems and evaluate LLM outputs for production.

India

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What I'm looking for

I'm looking for remote or contract GenAI work where I can build production RAG systems, evaluate LLM outputs, and apply multimodal AI to practical products.

I built and shipped DERMAS, an AI skin and hair diagnostic platform, end to end. Its multimodal RAG pipeline searches 500+ dermatology SKUs through pgvector with sub-2s retrieval.

At Ethara AI, I evaluated text-to-image outputs against RLHF/RLAIF rubrics as part of a team that reviewed 250,000+ outputs. I also designed a cross-evaluator annotation method to improve agreement and reduce noisy training labels.

I've built GenAI products spanning structured JSON prompting, computer vision, NLP, misinformation detection, OCR, and real-time translation. I use Python, LangChain, Hugging Face Transformers, FastAPI, Next.js, Supabase, and NVIDIA NIM to take AI systems to production.

Experience

Work history, roles, and key accomplishments

Research Intern, Healthcare AI

Multidisciplinary Centre for AI, Jamia Hamdard

May 2026 - Jul 2026 (2 months)

Applied the DERMAS multimodal computer vision and RAG pipeline to healthcare AI research under faculty mentorship. Presented the data strategy, architecture, and bias-stratified validation framework for the documented Fitzpatrick skin tone bias gap in dermatology AI.

Computer Vision RAG Healthcare AI Research Data Strategy Validation Framework

Post-LLM Training Intern

Ethara AI

Mar 2026 - May 2026 (2 months)

Evaluated text-to-image outputs against structured RLHF/RLAIF rubrics for aesthetic, compositional, and semantic quality, as part of a team that reviewed 250,000+ outputs over 3 months. Designed a cross-evaluator annotation method that improved inter-annotator agreement across batches and kept noisy labels out of downstream training runs.

LLM Evaluation RLHF RLAIF Annotation Pipelines Data Quality Inter Annotator Agreement

Product & Data Intern

M3M Foundation

Jun 2025 - Jul 2025 (1 month)

Cut an 8-hour weekly reporting cycle by about 30% with automated dashboards and data pipelines for the AEEP youth program (500+ participants). Surfaced 3 data-backed initiatives that leadership adopted.

Data Pipelines Dashboarding Automation Data Analysis Reporting

Education

Degrees, certifications, and relevant coursework

Jamia Hamdard University

B.Tech, Computer Science Engineering (AI/GenAI Specialization)

2022 - 2026

Grade: CGPA 7.73/10

Activities and societies: Dissertation: Outstanding (465/500)

Pursued a B.Tech in Computer Science Engineering with an AI/GenAI specialization, graduating in May 2026 with a CGPA of 7.73/10 and First Division with Distinction.