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VIVEK MANE

@vivekmane1

AI/ML engineer focused on production GenAI and enterprise RAG systems for finance and intelligent document intelligence.

India

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What I'm looking for

I’m looking for a role where I can build production-grade GenAI/RAG systems, ship reliable backend services, and improve model quality through evaluation and guardrails—especially for finance and document intelligence use cases.

I’m an AI/ML engineer who builds GenAI systems that actually work in production—shipping REST APIs, hybrid retrieval pipelines, and evaluation-focused agent experiences. In my AadiSwan Info Consultants internship, I developed a Financial Document Intelligence platform, built an MSME Credit Risk Assessment system, and supported a Bank Early Warning System by processing data from multiple ERP sources, reducing manual financial analysis effort by 40%.

On the product side, I built a production-grade multi-tenant NexusAI Enterprise RAG platform with Hybrid Retrieval (FAISS + BM25), cross-encoder reranking, and source-grounded citations, plus 5 enterprise AI agents with RBAC governance and an evaluation suite. I’m also developing FinSight AI, a Financial Intelligence Copilot on Azure with a FastAPI backend, hybrid RAG, and explainable credit risk modeling using XGBoost and SHAP, containerized for scalable deployment.

Experience

Work history, roles, and key accomplishments

AI/ML Engineer Intern

AadiSwan Info Consultants Pvt. Ltd.

Jan 2026 - Mar 2026 (2 months)

Developed a Financial Document Intelligence platform for banking use cases, enabling AI-powered document search, analysis, and insight extraction. Built an MSME Credit Risk Assessment system and engineered FastAPI/Django REST Framework REST APIs, also contributing to a bank early warning workflow that reduced manual financial analysis effort by 40%.

Machine Learning Document Intelligence Financial Analysis Credit Risk Assessment REST APIs fastAPI Django REST Framework Data Processing

Educational Data Analysis Intern

IIT Bombay

Jun 2025 - Oct 2025 (4 months)

Analyzed eye-tracking data from 38 students to study learning patterns and cognitive behavior using clustering to identify student groups with similar learning characteristics. Performed dimensionality reduction, feature analysis, and statistical evaluation to uncover factors influencing learning behavior and validate findings.

Eye Tracking Clustering Dimension Reduction Feature Analysis Statistical Validation