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AI Engineer with less than a year in LLMs & RAG
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Assessing your cultural and operational fit
AI/ML Engineer with hands-on experience building intelligent systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, and LangGraph. Proficient in end-to-end ML pipeline development – data ingestion, model training, and deployment. Skilled in Python, Deep Learning (ANN, CNN, RNN), NLP, and SQL. Currently pursuing M.E. in Computer Science. Passionate about building scalable, production-ready GenAI solutions that solve real-world problems. Quick learner with strong problem-solving and communication skills.
Mumtaz College of Engineering & Technology
B.Tech. · Computer Science & Engineering
N/A – June 30, 2025
Chicken Disease Prediction Project
June 24, 2026 – Present
Developed a deep learning-based chicken disease prediction system using Convolutional Neural Network (CNN) for image-based disease classification. Applied image preprocessing, feature extraction, and CNN model training to detect and classify poultry diseases accurately. The system enables early disease identification and helps improve poultry health management.
InsightForge AI
June 24, 2026 – Present
Built a production-grade RAG-based BI platform using FastAPI, Mongo DB, LangChain, and ChromaDB, with JWT-secured REST APIs to deliver intelligent document search, Q&A, and AI-generated business insights. Automated EDA, trend detection, and KPI dashboards using Pandas and Scikit-Learn, integrating OpenAI LLMs for executive summaries, and deployed via Docker with MLOps practices.
Rock vs Mine Sonar Signal Classifier
June 24, 2026 – Present
Developed a binary classification model to identify sonar signals as rock or underwater mine; applied feature scaling, model comparison, and evaluated using accuracy and confusion matrix analysis.
Apple Sales Data Analysis
June 24, 2026 – Present
Performed comprehensive EDA on Apple sales dataset to uncover revenue trends, seasonal patterns, and top-performing products; delivered actionable insights through rich data visualizations.
MLOPS Practitioner
Unknown
June 1, 2026 – Present
Data Science
TEKS Academy
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in AI/ML, ranging from academic deep learning applications (Chicken Disease Prediction, Rock vs Mine Classifier) to a more complex, production-grade RAG system (InsightForge AI). The 'InsightForge AI' project, being a personal initiative, shows proactivity and a drive to build practical solutions, which aligns well with an innovative culture. The breadth of technologies used (LLMs, RAG, MLOps, various databases, FastAPI) indicates a willingness to learn and adapt. However, the lack of professional experience and team-based projects limits the assessment of collaboration and broader cultural alignment.
Soft Skills & Operational Fit
The candidate's resume highlights 'strong problem-solving and communication skills' and a 'quick learner' attitude. Project descriptions are clear and concise, indicating good written communication. The 'InsightForge AI' project demonstrates an understanding of end-to-end system development and MLOps, which are crucial for operational fit in an AI engineering role. However, without specific behavioral assessment data, a deeper evaluation of soft skills and operational fit is limited.