← Back to list
Registration: 08.05.2025

Jeevan Chevula

Specialization: AI / ML Engineer
— An aspiring AI/ML Engineer with a strong foundation in Artificial Intelligence and Machine Learning, eager to apply my skills in developing cutting-edge AI solutions. — Proficient in Deep Learning and Python, with hands-on experience in optimizing business processes and building automation solutions. — Passionate about leveraging AI/ML to solve real-world problems and drive technological advancements.
— An aspiring AI/ML Engineer with a strong foundation in Artificial Intelligence and Machine Learning, eager to apply my skills in developing cutting-edge AI solutions. — Proficient in Deep Learning and Python, with hands-on experience in optimizing business processes and building automation solutions. — Passionate about leveraging AI/ML to solve real-world problems and drive technological advancements.

Portfolio

Intent-Based Chatbot

● Built an image classification model using MobileNetV2 to distinguish between cat and dog images. Key responsibilities included: ● Preprocessing and augmenting the dataset using Keras' ImageDataGenerator. ● Fine-tuning the pretrained MobileNetV2 model for improved accuracy on binary classification. ● Training and evaluating the model using TensorFlow/Keras. ● Visualizing training metrics (accuracy, loss) and confusion matrix to assess performance. ● Exporting the model for potential deployment in lightweight environments.

FAQ-Based RAG Chatbot

I designed and implemented the entire Retrieval-Augmented Generation (RAG) pipeline. This included: ● Converting FAQs into vector embeddings using sentence-transformers. ● Storing and indexing data using FAISS for fast similarity search. ● Creating the backend API with FastAPI to handle user queries. ● Retrieving relevant context using LangChain and generating answers with a Hugging Face transformer model. ● Testing and optimizing the system locally to reduce inference time and memory usage.

Image Classification

Built an image classification model using MobileNetV2 to distinguish between cat and dog images. Key responsibilities included: ● Preprocessing and augmenting the dataset using Keras' ImageDataGenerator. ● Fine-tuning the pretrained MobileNetV2 model for improved accuracy on binary classification. ● Training and evaluating the model using TensorFlow/Keras. ● Visualizing training metrics (accuracy, loss) and confusion matrix to assess performance. ● Exporting the model for potential deployment in lightweight environments.

Skills

Python
Machine learning
Artificial Intelligence
Deep learning
Computer vision
NLP
FastAPI
PyTorch
TensorFlow
Scikit learn
ChromaDB

Work experience

AI/ML Engineer
09.2022 - 06.2024 |HCL Tech
Python, Java, SQL, FAISS, ChromaDB, PyTorch, Scikit-learn, OpenCV
Image-Based Product Classification: ● Designed a MobileNetV2-based CNN with TensorFlow to classify retail products from shelf images. ● Used OpenCV for preprocessing and achieved 92% accuracy using transfer learning. ● Deployed the model via FastAPI, collaborated with cross-functional teams for integration, and supported performance monitoring in internal test environments. Invoice OCR & Field Extraction: ● Built an OCR-based system to extract structured data (Invoice No., Date, Total, Vendor) from semi-structured PDF invoices. ● Used Tesseract OCR with OpenCV preprocessing for improved text clarity. ● Trained a field classifier using TF-IDF and Logistic Regression with 91% extraction accuracy. ● Deployed via FastAPI and integrated with internal finance tools, reducing manual invoice processing by 60%. Product Expiry Date Detection from Packaging: ● Built an OCR-based system using Tesseract and OpenCV to automatically detect and extract expiry dates from printed labels on packaged goods. ● Applied preprocessing techniques such as grayscale conversion, blurring, and adaptive thresholding to enhance text clarity. ● Used regular expressions to validate extracted dates and flag expired products. ● Achieved high reliability in varied packaging conditions. ● Deployed the solution using FastAPI and integrated it with the warehouse dispatch system to prevent the shipment of expired items. Projects: 1. FAQ-Based RAG Chatbot. ● Developed a RAG FAQ Chatbot using LangChain, FAISS and ChromaDB for handling FAQ-based queries. Integrated both vector databases to optimize data retrieval and improve chatbot response accuracy. ● Implemented PDF-based document processing, allowing users to upload files and store embeddings for retrieval. ● Utilized Hugging Face embeddings and a Flan-T5 language model for accurate response generation. ● Built a FastAPI backend with endpoints for PDF uploads and query-based retrieval. ● Applied text chunking techniques using RecursiveCharacterTextSplitter to enhance retrieval accuracy. ● Gained hands-on experience in Vector Databases, LLMs, FAISS, and API development. 2. Intent-Based Chatbot. ● Developed a rule-based chatbot to handle predefined user intents such as greetings, product inquiries, and pricing details. ● Implemented intent-response mapping for structured and efficient query handling. ● Used Gradio to build an interactive user interface for real-time chatbot interactions. ● Applied Python and NLP techniques to improve chatbot response accuracy. ● Gained hands-on experience in chatbot development, user interaction design, and Gradio deployment. 3. Image Generation. ● Built a generative model leveraging Stable Diffusion to create high-quality images from textual descriptions. ● Utilized PyTorch and Hugging Face Diffusers library to implement the architecture and manage training workflows. ● Gained expertise in Generative AI, Stable Diffusion Models, and advanced Python programming for model implementation. 4. Object Detection. ● Designed and implemented an object detection system using the YOLO (You Only Look Once) algorithm to identify and classify objects in real-time. ● Used OpenCV and PyTorch to deploy the model for real-world applications, including live video feeds. ● Gained hands-on experience in Computer Vision, Deep Learning, and efficient object detection frameworks. 5. Image Classification. ● Developed an image classification model using MobileNetV2 as a feature extractor. ● Applied data augmentation techniques (rotation, shifting, zooming, flipping) to enhance model generalization. ● Utilized TensorFlow & Keras to train and fine-tune the model for improved accuracy. ● Processed images using ImageDataGenerator for efficient loading and augmentation. ● Evaluated model performance using test datasets and optimized hyperparameters for better results. ● Gained hands-on experience in Transfer Learning, Deep Learning, and ● Image Processing. 6. Movie Recommendation System. ● Developed a content-based recommendation system using a dataset of Telugu movies. ● Utilized TF-IDF (Term Frequency-Inverse Document Frequency) to extract key features from textual data and calculated cosine similarity to find relevant movies. ● Enhanced user experience by providing accurate and personalized movie recommendations based on user preferences. ● Gained hands-on experience in Python, Pandas, and Scikit-learn, with a focus on NLP techniques. 7. NLP Tasks Using Pretrained. ● Developed a comprehensive NLP system to perform tasks such as Sentiment Analysis, Named Entity Recognition (NER), and Text Generation using pretrained Transformer models from the Hugging Face Transformers library. ● Integrated models like BERT for NER and sentiment analysis, and GPT-2 for text generation, leveraging transfer learning for rapid deployment.

Educational background

Engineering and Technology (Bachelor’s Degree)
2017 - 2021
Sri Indu College

Languages

EnglishAdvancedHindiProficient