AI Engineer with less than a year in Machine Learning, LLM-based Applications & Backend Development.
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Computer Science undergraduate specializing in AI & ML with experience in machine learning, LLM-based applications, backend development, REST APIs, and scalable software solutions using Python, Java, Node.js, and Agile practices.
CMR Technical Campus, Hyderabad
B.Tech · Computer Science Engineering (Artificial Intelligence & Machine Learning)
August 1, 2022 – June 30, 2026
Google Cloud
Data Analytics Virtual Internship
July 1, 2025 – June 1, 2026
India
AI Knowledge Assistant using RAG
July 1, 2025 – June 1, 2026
Developed an AI-powered knowledge assistant capable of answering user queries from custom documents using Retrieval-Augmented Generation (RAG). Built a document ingestion pipeline to process PDFs and text files, generate embeddings, and store them in a FAISS vector database for semantic retrieval. Implemented prompt engineering techniques to improve response relevance and reduce hallucinations during LLM inference. Integrated REST APIs for query processing and retrieval workflows, enabling scalable backend communication. Evaluated retrieval quality and response accuracy through testing and optimization of chunking and embedding strategies. Utilized Python, LangChain, FAISS, and Large Language Models to create an end-to-end conversational AI application.
Thyroid Detection Using Machine Learning
January 1, 2025 – December 31, 2025
Built and benchmarked an end-to-end supervised ML classification pipeline across 3 algorithms (Random Forest, SVM, Naive Bayes) on a 6,962-record clinical dataset, with Random Forest achieving 99% accuracy. Applied SMOTE oversampling to address class imbalance, improving model generalization across hypothyroid, hyperthyroid, and euthyroid categories. Engineered and normalized 8+ clinical features (TSH, T3, T4, TT4, age, gender) using StandardScaler and LabelEncoder to standardize inputs and improve model performance. Evaluated models using accuracy, precision, recall, F1-score, and confusion matrices for interpretable healthcare prediction analysis. Developed a Tkinter-based GUI enabling dataset uploads, model training, and real-time disease prediction for non-technical users.
View ProjectAICTE Virtual Internship in Artificial Intelligence and Machine Learning
AICTE
June 1, 2026 – Present
Artificial Intelligence Certification
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Computer Science Engineering (AI & ML) and project work align well with an AI Engineer role. The diversity of projects, from traditional ML classification to modern RAG-based LLM applications, shows a breadth of interest within AI. The Google Cloud internship, even if virtual, indicates an interest in industry-standard tools and cloud environments. However, the lack of professional experience beyond virtual internships means the candidate's adaptability to a corporate culture and collaborative work environment is yet to be proven.
Soft Skills & Operational Fit
The candidate demonstrates a proactive learning attitude through academic projects and virtual internships. The project descriptions indicate an ability to work on complex technical problems and deliver functional solutions. The focus on user-friendly interfaces (Tkinter GUI) suggests an awareness of end-user needs. However, without direct interview data, specific soft skills like teamwork, problem-solving under pressure, or communication in a professional setting cannot be fully assessed.