Lavanya Kebbhalli Muruli — AI Engineer

About Me

I'm Lavanya Kebbhalli Muruli, an AI Engineer with over two years of experience in Generative AI and Machine Learning. I specialize in designing AI workflows and optimizing performance, always aiming to contribute scalable solutions that make a real impact.

Professional Experience

AI Automation Trainee

Fireflink

Feb 2026 – Apr 2026

Remote

Key Responsibilities & Tech:

Developed an AI-driven scriptless test automation solution using Rapid Agile Testing Software to automate functional testing for the ShopperStack web application.
Leveraged NLP-based automation to execute end-to-end functional and regression test scenarios, reducing manual testing effort by approximately 80% and enhancing test execution consistency.
Identified, documented, and validated application defects prior to simulated production release, contributing to improved software quality and test coverage.
Designed reusable AI automation workflows that accelerated regression testing cycles and enabled faster validation of application updates without writing code.
Collaborated in an Agile environment to analyze requirements, evaluate AI automation performance, and present project outcomes, enhancing skills in AI-powered quality assurance and software testing.

AI-Powered Supplier Invoice Assistant

Remote, UK

Feb 2025 – Apr 2025

Remote, UK

Key Responsibilities & Tech:

Designed and implemented an AI-powered document intelligence solution using GPT-4, LangChain, RAG, and Vector Databases to automate supplier invoice processing and information retrieval.
Developed Retrieval-Augmented Generation (RAG) workflows and intelligent retrieval pipelines to enable accurate, context-aware responses through natural language queries.
Optimized LLM performance using prompt engineering techniques for anomaly detection, duplicate invoice identification, and payment status analysis.
Integrated OCR, semantic search, and AI-driven retrieval architectures to enhance intelligent document processing and decision-making.
Collaborated with stakeholders to translate business requirements into scalable AI solutions and technical workflows.
Reduced manual invoice analysis effort by over 70% through AI automation and retrieval-based systems.

Artificial Intelligence Intern

Ignite Intern

Jun 2024 – Jul 2024

Remote, UK

Internship

Key Responsibilities & Tech:

Developed speech recognition models using CNN and RNN architectures with TensorFlow and Keras to support accurate speech-to-text transcription.
Built AI-powered chatbot solutions using Python and APIs for text summarization, CV generation, and interview preparation assistance.
Applied Machine Learning, NLP, and Conversational AI techniques to enhance solution performance and user experience.
Collaborated with multidisciplinary teams to develop, test, and communicate AI solutions aligned with business requirements.

Graduate Engineer Intern

QSpiders Campus

Nov 2023 – Dec 2023

India

Internship

Key Responsibilities & Tech:

Applied Java and SQL fundamentals to develop software solutions and improve application reliability through exception handling techniques.
Utilized DQL, DDL, and DML operations to manage, query, and maintain structured datasets within relational database environments.
Strengthened software engineering and problem-solving capabilities through hands-on application development and DBMS.

Graduate Engineer Intern

Skill Bout

Oct 2023 – Nov 2023

India

Internship

Key Responsibilities & Tech:

Developed modular and reusable software solutions using Python programming and function-based design principles.
Applied recursion techniques to solve computational problems and improve code efficiency.
Utilized object-oriented programming principles to develop scalable and maintainable software applications.
Enhanced software development and analytical skills through practical implementation of core programming concepts.

Education & Certifications

Academic Background

M.Sc. Artificial Intelligence

Queen Mary University of London

Sep 2023 - Sep 2024

Merit

B.Tech in Computer Science and Engineering

VTU

Aug 2019 - Aug 2023

Grade: 7.97/10

Featured Projects

D

Development and Evaluation of a Stock Prediction Model Using LSTM Neural NetworksCheck out ↗

Developed and evaluated machine learning and deep learning models, including Linear Regression, RandomForest, and LSTM Neural Networks, to predict stock prices using historical market data.
Applied Python, TensorFlow, Keras, Pandas, and Scikit-learn to build, train, and validate predictive models using RMSE and cross-validation techniques.
Analyzed model performance and compared forecasting accuracy across multiple algorithms to support data-driven decision-making.
Demonstrated strong capabilities in machine learning, model evaluation, and statistical analysis through end-to-end AI solution development.

D

Deeper Networks for Image Classification

Researched and evaluated deep learning architectures, including GoogLeNet and VGG, for image classification tasks.
Analyzed model performance using benchmark datasets such as MNIST and CIFAR-10 to assess classification accuracy and effectiveness.
Applied deep learning techniques to compare network architectures and identify optimal approaches for image recognition problems.
Strengthened expertise in computer vision, neural networks, and AI model evaluation through practical experimentation and analysis.