Generative AI Engineer with less than a year in LLMs & RAG systems
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Passionate AI Engineer with hands-on experience in Generative AI, LLMs, RAG systems, and Agentic AI. Skilled in building end-to-end intelligent applications and developing scalable AI solutions for real-world business challenges.
Noida Institute of Engineering and Technology(NIET)
Master of Computer Applications · Computer Applications
N/A – June 30, 2026
Medigence
Junior AI/ML Developer
October 1, 2025 – March 1, 2026
Noida, Uttar Pradesh, India
Self-Healing Hallucination Detection & Correction Multi-Agent System
June 24, 2026 – Present
Designed and developed a LangGraph-based multi-agent pipeline that detects LLM hallucinations through claim extraction, multi-source evidence retrieval, BM25 evidence ranking, and DeBERTa NLI verification, producing a claim-level hallucination score. Implemented a self-healing correction mechanism that rewrites only hallucinated claims using verified evidence, reconstructs the original response, and performs iterative re-verification (up to 3 iterations) to improve factual consistency while preserving the original answer structure.
Multi-User RAG Chatbot for Company Policy Assistance
June 24, 2026 – Present
Developed and deployed a multi-user RAG-based chatbot that enables organizations to upload company policy documents and provides accurate,context-aware answers to employee queries using LangChain, FAISS,Cohere Embeddings, Groq LLMs,FastAPI and MongoDB Built a production-ready full-stack AI application featuring semantic document retrieval, protected user sessions,scalable backend APIs and a React+Tailwind CSS frontend, helping employees quickly understand HR policies, leave rules etc.
View ProjectFull Stack Data Science Pro
PW Skills
May 1, 2024 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in cutting-edge AI technologies and a drive to build practical, real-world solutions. The 'Self-Healing Hallucination Detection & Correction Multi-Agent System' project showcases an innovative mindset and a willingness to tackle challenging problems, which aligns well with a dynamic, research-oriented AI engineering environment. The 'Multi-User RAG Chatbot' project indicates an ability to work on full-stack applications, suggesting adaptability and a broader understanding of product delivery. The candidate's educational background and certification in 'Full Stack Data Science Pro' further support a continuous learning mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and problem-solving approach, particularly in addressing complex issues like LLM hallucination. The deployment of a multi-user RAG chatbot suggests an understanding of end-to-end product development and user-centric design. The experience, though limited in duration, shows an ability to contribute to backend features and integrate APIs, which are crucial for operational roles.