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AI Engineer with less than a year in Generative AI and LLM systems
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AI/ML Engineer specializing in Generative AI, LLMs, and Agentic AI systems with expertise in building multi-agent frameworks, RAG pipelines, and autonomous workflows. Skilled in Python, LangChain, LangGraph, AutoGen, and FastAPI, with experience deploying scalable AI solutions on cloud platforms. Passionate about advancing next-generation intelligent systems that combine reasoning, retrieval, and human-AI collaboration.
Sal institute of tech & Engg research Ahmedabad
B.Tech · computer engineering
May 1, 2024 – Present
Government Polytechnic, Ahmedabad
Diploma · computer engineering
August 1, 2021 – May 1, 2024
VertxAI
AI/ML Intern
March 1, 2025 – August 1, 2025
India
Founder-Investor Matching AI System
June 5, 2026 – Present
Designed an AI-powered matching engine that connects startup founders with suitable investors using semantic search and vector embeddings. Implemented LangChain + LangGraph pipelines to handle query understanding, ranking logic, and multi-agent reasoning for better match quality. Used FAISS vector database to retrieve the top 100 best matches for each founder profile based on contextual embeddings. Developed a FastAPI backend with WebSocket streaming, ensuring low-latency interactive voice experiences. Stored and managed match results in MongoDB, enabling efficient tracking, filtering, and analytics on investor-founder interactions. Containerized with Docker and deployed on GCP (Cloud Run) for scalable, production-ready usage.
ArXiv Research Agent
June 5, 2026 – Present
Built an AI-powered multi-agent research assistant using AutoGen, capable of retrieving, summarizing, and answering questions on ArXiv papers. Designed a multi-agent architecture where specialized agents handled retrieval and summarization collaboratively. Leveraged LLMs for paper understanding and concise, user-friendly outputs. Developed an interactive Streamlit interface, enabling seamless research exploration and structured outputs (JSON/Markdown). Containerized with Docker and deployed on Hugging Face Spaces for public accessibility and reproducibility.
DataAnalyzerAI
June 5, 2026 – Present
Built an AutoGen-based multi-agent system that lets users upload CSV/JSON data and query it in natural language. Implemented agents that generate, execute, and return Python code to produce analysis and graph visualizations automatically. Delivered an end-to-end data exploration tool with reproducible results using Dockerized deployment.
AI-powered Mock Pitch Voice Agent
June 5, 2026 – Present
Built an AI-driven voice agent to simulate startup pitch Q&A sessions, helping founders practice and refine investor communication. Integrated speech-to-text (Whisper) and text-to-speech (elevenlabs TTS) for real-time interactive conversations. Designed LangChain + LangGraph-based agentic workflows for context-aware questioning, evaluation, and adaptive conversation flow. Developed a FastAPI backend with WebSocket streaming, ensuring low-latency interactive voice experiences. Stored and analyzed conversation data in MongoDB, enabling performance tracking and feedback insights for continuous improvement. Deployed on GCP (Cloud Run + Docker) with scalable architecture and modular design for easy feature expansion.
Cultural Fit Analysis
The candidate demonstrates a strong passion for advancing next-generation intelligent systems, aligning well with an innovative and research-oriented culture. The diverse range of personal projects (voice agents, matching systems, research assistants, data analyzers) showcases a proactive learning attitude and a broad interest in applying AI to different problem domains. The use of open-source tools and deployment on platforms like Hugging Face Spaces suggests a collaborative and community-oriented mindset. However, the candidate's experience is primarily academic and personal projects, with a future-dated internship, which might indicate a need for more exposure to corporate team environments and large-scale production systems. The breadth of skills and technologies used across projects indicates adaptability and a willingness to explore new tools.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as Problem-Solving, Research & Experimentation, Collaboration, Adaptability, Rapid Prototyping, and Critical Thinking. These are crucial for an AI Engineer role, especially in a fast-evolving field. The project descriptions indicate an ability to work independently on complex systems and integrate various technologies, suggesting good operational fit for roles requiring initiative and technical depth. The internship experience, though future-dated, also emphasizes collaboration with senior engineers and CI/CD practices, indicating an understanding of team dynamics and modern development workflows.