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Generative AI Engineer with less than a year in LLMs & RAG
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Computer Science graduate (2026) specializing in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Python development. Hands-on experience building production-grade AI systems with LangChain, LangGraph, FastAPI, Vector Databases, Prompt Engineering, and Cloud platforms. Built AI-powered applications serving 2,000+ users, automated 500+ queries, and published research in multilingual NLP. Proficient in the full GenAI stack from embedding pipelines and LLM integration to REST API deployment with Docker and cloud infrastructure. Eager to contribute to Polluxa's mission of shaping the future of AI-powered commerce.
Acharya Nagarjuna University
B.Tech · Computer Science and Engineering
August 1, 2022 – June 30, 2026
PANTECH Solutions
AI/ML Intern
August 1, 2025 – November 1, 2025
India
RVR & JC AI Assistant
June 1, 2026 – Present
Built a production-ready Generative AI chatbot using LLMS, RAG pipelines, and LangChain serving 2,000+ users. Designed vector embedding pipelines to enable semantic retrieval over institutional knowledge bases. Integrated FastAPI backend with LLM-powered conversational engine; deployed on cloud infrastructure. Automated 500+ FAQ responses, reducing administrative workload by 40% and improving accuracy by 30%.
QuIM-RAG: Retrieval-Augmented Generation System
June 1, 2026 – Present
Architected an end-to-end RAG system using quantized embeddings and Meta-Llama3-8B-Instruct for domain QA. Implemented vector database retrieval with question-to-question optimization for enhanced semantic search. Applied advanced prompt engineering techniques to improve context grounding and answer relevance. Evaluated system performance with RAGAS and BERTScore; iterated on retrieval and ranking strategies.
View ProjectMultilingual NLP Framework for Code-Mixed Text
June 1, 2026 – Present
Built a Dynamic Prompt Learning framework on XLM-R using Adapter Prompt modules for multilingual classification. Applied prompt engineering and adapter-based fine-tuning for sentiment analysis, hate speech, and humor recognition. Enhanced multilingual LLM understanding across Hindi-English code-mixed datasets; led to peer-reviewed publication.
Handwritten Digit Recognition using CNN
June 1, 2026 – Present
Developed a CNN-based image classifier on MNIST achieving 98.5% accuracy using TensorFlow. Built end-to-end preprocessing, training, and evaluation pipelines demonstrating strong AI/ML fundamentals.
Oracle Cloud Infrastructure Generative AI Professional
Oracle
June 1, 2026 – Present
NVIDIA – Building RAG Agents with LLMs
NVIDIA
June 1, 2026 – Present
DeepLearning.AI - Fine-Tuning Large Language Models
DeepLearning.AI
June 1, 2026 – Present
Databricks - Generative AI Fundamentals
Databricks
June 1, 2026 – Present
AWS Certified Cloud Practitioner
AWS
June 1, 2026 – Present
Microsoft Azure Fundamentals (AZ-900)
Microsoft
June 1, 2026 – Present
NPTEL The Joy of Computing Using Python (Silver Medal)
NPTEL
June 1, 2026 – Present
Kaggle Python Coder Badge
Kaggle
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, including RAG systems, multilingual NLP, and a production chatbot, aligns well with an innovative and research-oriented environment. Their focus on Generative AI and LLMs directly matches the target role. The breadth of skills across various AI/ML frameworks, databases, and cloud platforms suggests adaptability and a willingness to explore different technologies, which is beneficial for cultural fit in a dynamic tech company.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and a proactive learning attitude through numerous certifications and academic projects. Their experience in building user-facing AI applications and contributing to research suggests good problem-solving and collaboration potential. The internship experience, though brief, indicates familiarity with standard software development practices like version control and API integration.