AI Engineer with 1+ years in Multi-Agent LLM Systems & RAG Pipelines
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AI/ML Engineer with hands-on experience building multi-agent LLM systems and RAG pipelines in production. Currently designing finance-domain multi-agent AI chatbots at Big Air Lab using custom Python SDK architectures.
Rajiv Gandhi University of Knowledge Technologies
Bachelor Of Technologies · Computer Science and Engineering
August 1, 2021 – June 30, 2025
Rajiv Gandhi University of Knowledge Technologies
Pre-University Course · Physics, Chemistry, Mathematics
August 1, 2019 – June 30, 2021
Zilla Parishath High School
Secondary School Certificate (SSC)
June 1, 2018 – May 31, 2019
Big Air Lab
AI/ML Intern
June 1, 2025 – Present
Bengaluru, Karnataka, India
Rava.ai
Associate AI/ML Intern
December 1, 2024 – April 1, 2025
Hyderābād, Telangana, India
NIT Warangal
Summer Internship
May 1, 2024 – June 1, 2024
India
Deep Learning Based Effective Reverberation And Noise Suppression For Enhancing The Target Detection In Active Sonar Systems
June 25, 2026 – Present
In real-time sonar signal processing, target echoes are often observed with reverberation and noise, which can suppress the desired target signal, especially in shallow water surfaces. The high levels of reverberation in shallow water can limit the performance of active sonars. In this project, the application of deep learning techniques such as Convolutional Neural Networks (CNN) was explored and used to enhance the Signal-to-Reverberation Ratio (SRR). My approach aims to suppress the reverberation and improve the efficiency of target identification in varying acoustic conditions.
Bank Customer Churn Analysis
June 25, 2026 – Present
The goal of bank customer churn analysis is to develop predictive models that can accurately predict which customers are likely to churn in the future. By identifying these customers in advance, banks can take proactive measures to retain them and reduce churn. A model was trained in various classification and regression algorithms.
Multimodal RAG System (Text + Image)
June 25, 2026 – Present
Designed a multimodal RAG pipeline that accepts any file format as input. For image inputs: Generated detailed captions/descriptions using image-capable models. Converted visual data into textual representations. Created embeddings for both text chunks and generated image captions. Stored and retrieved vectors using Qdrant. Delivered accurate, context-aware responses through retrieval-augmented generation. Achieved evaluation performance with answer relevance up to 80% and faithfulness in the 60-70% range.
Retrieval Augmented Generation Chatbot
June 25, 2026 – Present
Web URL-based Chatbot: Built using LangChain and Groq LLMs with FAISS for vector storage, BeautifulSoup for web scraping, and Streamlit for deployment; retrieved and answered user queries based on indexed website content. PDF-based Chatbot: Implemented with LangChain, Groq, FAISS, and PyMuPDF4LLM using Recursive Character Text Splitter for chunking; accurately retrieved and displayed 6 relevant chunks with page numbers in response to user queries.
IR4.0 Foundation
TechSaksham
June 1, 2026 – Present
Data Science and Career guidance
RGUKT Srikakulam
November 2, 2024 – Present
Leveraging Generative AI effectively for programming tasks
RGUKT Srikakulam
October 29, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects and internships demonstrate a strong interest and focus on Generative AI and Machine Learning, aligning well with the target role of a Generative AI Engineer. The diversity of projects, from multimodal RAG systems to finance-based chatbots and sonar signal processing, indicates a broad technical curiosity and willingness to explore different applications of AI. Participation in workshops and certifications further shows a proactive approach to learning and staying updated with industry trends. The involvement in extracurricular activities like organizing technical fests suggests a collaborative and engaged individual, which is positive for cultural fit.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as 'Team Collaboration', 'Leadership', and 'Problem Solving'. Project descriptions indicate an ability to work in teams and adapt to new architectures. The experience at Big Air Lab shows problem-solving skills in identifying LangGraph limitations and designing a custom SDK. The role as a Training and Placement Cell Student Coordinator and Samavedhan Event Organizer further supports leadership and collaboration. These attributes suggest a good operational fit for a collaborative engineering environment.