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AI Engineer with less than a year in Machine Learning & Generative AI
Aspiring AI Engineer with hands-on experience in Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI Agent development. Skilled in building end-to-end AI applications using Python, LangChain, LangGraph, FastAPI, Vector Databases, and Machine Learning frameworks. Experienced in developing scalable AI solutions, integrating LLMs into applications, designing RAG pipelines, and deploying AI-powered systems. Strong problem-solving abilities with a passion for building intelligent and production-ready AI applications.
Innomatics Research Lab
Data Science · Python, Statistics, Data Analysis, SQL + PowerBI, ML, DL, GenAI, NLP, Computer Vision, LLM
August 1, 2025 – June 30, 2026
Vignana Bharathi Institute of Technology
B.Tech · AI&ML
August 1, 2021 – June 30, 2025
Unknown
Generative AI Intern
October 1, 2025 – Present
Hyderābād, Telangana, India
Advi Groups
Intern
February 1, 2025 – September 1, 2025
Hyderābād, Telangana, India
Personalized Predictive Healthcare Using Machine Learning & Generative AI
March 1, 2025 – June 1, 2026
Designed and deployed an end-to-end ML pipeline to predict diabetes and heart disease risk using Logistic Regression, Random Forest, and XGBoost, achieving 80-82% accuracy across multiple validation folds. Processed and engineered features from 5,000+ patient records, improving model performance by 18% through advanced preprocessing, feature selection, cross-validation, and GridSearchCV. Integrated Generative AI (LLMs) to convert model predictions into personalized healthcare insights and recommendations, reducing clinical interpretation time by 40%.
View ProjectSelf-Healing Multi-Agent System
March 1, 2025 – June 1, 2026
Built an AI-powered Self-Healing Multi-Agent System that autonomously detected, fixed, and retried failed code workflows, reducing manual debugging effort by 70%. Implemented 4+ autonomous AI agents using LangGraph for debugging, validation, runtime execution, and intelligent recovery, improving workflow reliability by 85%. Integrated Google Gemini API, fallback AI architecture, and runtime validation to achieve 90%+ successful recovery rate for failed executions. Developed and deployed an interactive Streamlit-based AI debugging platform with screenshot analysis and workflow timeline visualization, improving issue diagnosis speed by 60%.
View ProjectAI-Powered Video-to-Web Content Generator
March 1, 2025 – June 1, 2026
Built an end-to-end system converting 100+ YouTube videos into structured articles and websites. Extracted transcripts (Hindi/English) and generated content using LLMs with ~95% accuracy. Implemented recursive summarization for long videos (30-120 mins), improving coverage by ~35%. Reduced manual effort by ~70% and processing time by ~60%. Generated SEO-friendly articles (800-1500 words) with downloadable website (ZIP) output.
View ProjectCultural Fit Analysis
The candidate's academic projects demonstrate initiative and a proactive approach to learning and applying advanced AI concepts. The diversity of projects (healthcare, content generation, autonomous systems) suggests a broad interest in AI applications and a willingness to tackle different challenges. The focus on building 'production-ready AI applications' aligns with a results-oriented culture. The candidate's profile as an 'AI Engineer Aspirant' indicates a strong desire to grow and contribute in the AI domain.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving abilities, a passion for building intelligent systems, and an understanding of real-world impact (e.g., reducing manual effort, improving accuracy). The self-healing multi-agent system project demonstrates an aptitude for building robust and reliable systems, which is crucial for operational fit. However, without direct assessment data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively evaluated.