Aspiring AI Summer Intern Resume

Vedant Sarvesh Ranade

Boston, MA, ***** 339-***-**** ******.*@************.*** github.com/vrmaverick Education

Northeastern University, Khoury College Of Computer Science Boston, MA Pursuing Master of Science in Artificial Intelligence, Concentration in Machine Learning Sep. 2025 – May.2027

• Coursework: Linear Algebra and Probability, Foundations of AI and Algorithms University of Mumbai Mumbai, India

Bachelor’s in Artificial Intelligence and Data Science; GPA 3.74/4.00 Nov. 2021 – May 2025

• Coursework: Deep Learning, Reinforcement Learning, Statistics, Cloud Computing, and Big Data Analytics Technical Skills

Languages: Python, JavaScript, C, SQL(Postgres), MongoDB Machine Learning: Scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face, Ensemble learning, Bagging, Boosting Deep Learning: CNN, Sequence Modeling, Timeseries Forecasting, Generative Models, OpenCV, Transformers Natural Language Processing : LLMs, VLMs, TF-IDF, NER, LangChain, Ollama, RAG, AI Agents Data Science: Pandas, NumPy, Matplotlib, Seaborn, Data Cleaning, Feature Engineering Deployment: Google Cloud Platform, Azure, Streamlit, LangSmith, Tensorboard, AWS, Git Familiar with: ReactJS, FastAPI, Flask, Node.js, WordPress, Cursor, Firestore, Algorithms, Mathematics, Statistics Experience

Python Developer Intern Jun. 2025 – Jul. 2025

Web creations, Dombivali, India

• Upgraded management portal using Flask and SQL for real-time data visualization.

• Implemented a 3D rendering model using transformers, solving a complex technical requirement for the client.

• Optimized workflows for large data directories, proposing architectural upgrades to improve system scalability. Web Developer Intern Dec. 2023 – Dec. 2023

Web creations Dombivali, India

• Developed and maintained a PHP-based backend automation and ensured efficient data management using SQL.

• Customized websites as per evolving requirements, improving user experience, and demonstrating adaptability.

• Collaborated cross-functionally with design and development teams to accelerate project delivery and quality. Projects

Mint-Sage : AI Finance Manger NLP, Pytorch, Streamlit, Tensorflow Oct. 2025 – Dec. 2025

• Built an AI finance assistant that automates ingestion, categorization, and forecasting to improve financial decisions.

• Engineered a MiniLM + SVM/XGBoost ensemble to classify 20 expense categories from noisy text.

• Developed an RF/Ridge ensemble on sliding-window features to forecast 30-day expenses, outperforming other models.

• Applied kertosis, skewness and beta-distribution–driven normalization to improve stability in 30-day expense forecasts.

• Designed an LLM budgeting layer that converts income, volatility, and forecast metrics into targeted recommendations. EduBoost: AI Based Educational Tools Tensorflow, ReactJS, Azure, FastAPI Sep. 2024 – May 2025

• Led AI research and model fine-tuning to enhance the performance of various existing supervised models.

• Developed a sketch-based math solver (like iOS 18) using CNNs capable of interpreting handwritten equations.

• Fine-tuned the Caffe model for illustration colorization, to achieve smoother and more realistic gradients.

• Integrated Transformer models BLIP and CLIP to generate an illustration summary.

• Designed a full-stack solution with ReactJS frontend, Firebase authentication, and MongoDB backend.

• Deployed all AI models on Azure, ensuring scalability and high availability. Deepfake Image Detection Python, Flask, Azure, Tensorflow, PIL Jan 2024 – May. 2024

• Architected a custom CNN model achieving 92% accuracy in detecting deepfake images.

• Built and deployed a Flask-based web application for real-time deepfake verification.

• Designed and deployed an API for seamless integration of detection services.

• Deployed the system on an Azure Virtual Machine, ensuring high-performance cloud execution. AI Based Navigation System for Drones Python, Unity, TCP Sockets, Flask Sep. 2023 – Dec 2023

• Engineered an AI-driven drone navigation system in Unity, with Python-based control.

• Evaluated and benchmarked multiple AI algorithms across key performance metrics to ensure robustness.

• Established real-time communication between the Unity simulation and Python backend using TCP sockets.

• Developed a Flask-based dashboard with automated SMTP notifications for a real-time Parcel Delivery app. Medical assistance Chat-bot LangChain, Ollama, Streamlit Dec. 2025 – Jan. 2026

• Designed a Streamlit chatbot that lets users ask health questions and receive structured, medically grounded information.

• Built a LangChain RAG pipeline over medical descriptions and Q&A pairs so the chatbot can retrieve relevant context.

• Integrated Ollama-hosted OpenSource LLMs and embeddings to chunk and retrieve the data efficiently.

• Safety-focused prompting and disclaimers to guide possible conditions and next steps. Demand Based Dynamic Pricing Using AI Python, Scikit-Learn, Streamlit Jan. 2024 – May 2024

• Developed a high-dimensional ensemble model using Scikit-Learn for demand forecasting and price optimization.

• Integrated real-time data of weather and temporal APIs on location to dynamically adjust pricing strategies.

• Automated pricing, invoicing, with UPI through a Streamlit dashboard, streamlining the workflow. FinComplaints: Financial Complaints Management NLP, Streamlit, Tensorflow Oct. 2024 – Dec. 2024

• Built a Streamlit app to let users search, filter, and analyze financial complaint narratives for faster issue triage.

• Cleaned and tokenized complaint text with custom NLP preprocessing to remove noise before modeling.

• Trained a TensorFlow text-classification model to route complaints into key product categories.

• Exposed model predictions and confidence scores in the UI, easier to review borderline or high-risk complaints. Youtube Comment Analysis Python, TensorFlow, Keras, TextBlob, Streamlit May. 2025 – Jun. 2025

• Engineered an end-to-end NLP pipeline achieving 93% accuracy in intent classification.

• Integrated BiRNN-based intent classification and TextBlob sentiment analysis on YouTube comments.

• Automated comment Extraction and analyzed real-world comments via YouTube API and visualized sentiments,

• Deployed a streamlit web app, enabling real-time comment analysis and user interaction.

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