AI Engineer with 2+ years in GenAI, LLM Systems & Backend Development
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AI Engineer specializing in Generative AI, LLM Systems, and Backend Development. I have experience designing and deploying LLM-powered backend systems, building RAG pipelines with vector databases, and integrating advanced AI capabilities. My expertise spans FastAPI, LangChain, Kubernetes, and various databases, focusing on creating scalable and efficient AI solutions for real-world business problems.
Shri Vaishnav Vidyapeeth Vishwavidyalaya
Bachelor of Technology · Computer Science
August 1, 2020 – June 30, 2024
Qlaws.ai
Founding AI Engineer
January 1, 2026 – Present
India
Fealty Technology
SDE-1 Developer
September 1, 2024 – January 1, 2026
India
Zangoh
Full-Stack Developer Intern
February 1, 2024 – June 1, 2024
India
Smart Pharmacy & Patient Medication Management System
February 1, 2024 – June 1, 2024
Developed a healthcare management platform consisting of two integrated applications: a pharmacy inventory system for medical stores and a patient medication reminder app to improve medicine adherence and pharmacy operations. Built medicine inventory management system with real-time stock tracking and automated low-stock alerts. Implemented sales, purchase, and supplier management modules for pharmacy business operations. Developed medicine reminder scheduling system with customizable notifications for patients. Integrated OCR-based prescription scanning to automatically extract medicine details from prescriptions. Designed analytics dashboards for sales insights, medicine usage tracking, and inventory reports. Ensured secure data storage and privacy-first architecture for healthcare-related data. Streamlines pharmacy inventory management while improving patient medication adherence through automated reminders and prescription digitization.
Legal Document Drafting System (AI Agents)
September 1, 2023 – September 1, 2024
Developed an AI-powered legal drafting platform that automatically generates structured legal agreements such as Share Purchase Agreements (SPA) from Letters of Intent (LOI). Built a multi-agent architecture for legal document drafting including extraction, clause retrieval, drafting, and validation agents. Implemented RAG-based clause retrieval system using vector databases to fetch legally relevant clauses. Designed structured prompt frameworks for clause-by-clause legal drafting. Developed APIs using FastAPI to handle document ingestion, clause retrieval, and automated draft generation. Enabled user-driven refinement workflows for modifying generated legal documents. Reduced legal drafting time from hours to minutes while maintaining consistency and jurisdiction-aware clause generation.
Legal Document Intelligence System - PageIndex Agentic RAG
September 1, 2023 – September 1, 2024
Built a production-grade legal AI system for analyzing complex contracts using a hierarchical PageIndex retrieval architecture instead of traditional vector embeddings. Implemented PageIndex hierarchical tree retrieval for document-aware reasoning without vector databases. Designed a multi-agent pipeline for legal analysis including clause understanding, risk detection, validation, and negotiation drafting. Developed a legal ontology engine with 20+ clause types and dependency graphs to detect contradictions and missing clauses. Built a jurisdiction-aware reasoning system supporting New York, Delaware, California, UK, and India legal standards. Implemented financial risk simulation models to estimate potential exposure and mitigation impact. Added explainability pipelines generating step-by-step reasoning traces for legal conclusions. Developed a FastAPI backend and Streamlit dashboard for contract analysis and executive reporting. Enabled structured reasoning over legal documents with hierarchy-aware retrieval and ontology-based risk detection, improving explainability compared to traditional vector RAG systems.
AI Agent: Natural Language to SQL Query Chatbot
January 1, 2023 – September 1, 2023
Built an AI-powered data assistant that allows users to interact with structured databases using natural language. Designed an LLM-based pipeline to convert natural language queries into optimized SQL queries. Implemented schema-aware query generation to handle joins, filters, and aggregations. Developed secure query execution pipelines to prevent invalid or unsafe SQL queries. Built interactive visualizations and dashboards for returned data insights. Integrated conversational memory for improved query context. Makes complex database analytics accessible to non-technical users without SQL knowledge.
Software Engineer Certificate
Hackerrank.com
January 1, 2024 – Present
Machine Learning with Python
Internshala
January 1, 2023 – Present
Python Certificate
Hackerrank.com
January 1, 2022 – Present
C/C++ Certificate
Codex cider, Indore
January 1, 2021 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from healthcare management to advanced legal AI systems and natural language to SQL chatbots, indicates a broad interest in applying AI across different domains. This diversity, coupled with experience in both startup (Qlaws.ai) and established tech environments (Fealty Technology), suggests adaptability and a willingness to tackle varied challenges. The emphasis on building production-grade systems and optimizing workflows aligns with a results-driven culture. The candidate's continuous learning through certifications and proactive exploration of emerging technologies demonstrates a growth mindset, which is crucial for cultural fit in an innovative AI role. The focus on secure and privacy-first architectures also indicates a responsible and ethical approach to development.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a strong ability to translate complex AI concepts into practical, impactful solutions. The experience in a 'Founding AI Engineer' role suggests an entrepreneurial mindset, adaptability, and a willingness to take ownership. The detailed descriptions of multi-agent systems and hierarchical retrieval demonstrate strong analytical and design thinking. The focus on reducing operational time and improving efficiency through AI automation highlights a business-oriented perspective. The candidate's ability to work with various technologies and integrate emerging solutions suggests a continuous learning mindset and good operational fit for dynamic environments.