Hi there, I'm

Sri Ganesh Arjula

Machine Learning and Generative AI Engineer specializing in deploying real-time predictive models, NLP solutions, and scalable AI systems. Expert in LLM fine-tuning, cloud platforms, and optimizing business operations with AI-driven solutions.

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< About Me />

I am Sri Ganesh Arjula, a passionate Machine Learning and AI Engineer with a knack for developing innovative solutions that drive business transformation. With a strong foundation in deploying real-time predictive models and NLP solutions, I excel in creating scalable AI systems that enhance operational efficiency. My expertise spans across various industries, including logistics, healthcare, and banking, where I have been recognized for my contributions to AI and IoT projects. I am committed to leveraging AI to solve complex problems and am always eager to explore new technologies and methodologies.

Traditional ML Algorithms
Scikit-learn, XGBoost, LightGBM, Random Forests, SVM
Deep Learning
TensorFlow, PyTorch, Keras, CNNs, RNNs
Computer Vision
OpenCV, YOLO, Object Detection, Image Segmentation
MLOps & Deployment
MLflow, Docker, Kubernetes, FastAPI, Model Monitoring
Cloud ML Services
AWS SageMaker, Azure ML, Google Vertex AI
Big Data Processing
Spark, Hadoop, Kafka, Data Pipelines
Large Language Models
LLaMA, GPT, BERT, Transformers, Fine-tuning
Gen AI Frameworks
LangChain, Hugging Face, OpenAI API, Anthropic API
Prompt Engineering
RAG, Few-shot Learning, Chain of Thought, System Prompts
Gen AI Infrastructure
vLLM, Text Generation Inference, Model Quantization
Multimodal Gen AI
Stable Diffusion, DALL-E, GPT-4V, Image Generation
Gen AI Tools
LlamaIndex, Semantic Kernel, Vector Databases

< Work Experience />

Machine Learning / Generative AI Engineer

ArcBest Corporation, Fort Smith, AR

Jul 2024 – Present
  • ▹ Designed and implemented an AI-driven framework utilizing AWS Textract to extract text from customer service records in PDFs and images, which was then analyzed using Llama 3.1.
  • ▹ Optimized Llama 3.1 using LoRA/PEFT fine-tuning techniques, improving the accuracy of text classification and summarization by 40% for compliance audits.
  • ▹ Developed a Python-based data pipeline integrating AWS Textract with Pandas and Apache Kafka, enabling seamless extraction, transformation, and ingestion of document data into the AI auditing system.
  • ▹ Deployed the fine-tuned Llama 3.1 model using Hugging Face Transformers on AWS SageMaker, ensuring scalable and efficient real-time inference.
  • ▹ Integrated MLflow and TensorBoard to monitor and refine model performance, ensuring compliance with auditing standards and reducing manual review time by 35%
PyTorch AWS Azure ML Kubernetes

Machine Learning Engineer

Arkansas Blue Cross and Blue Shield, Little Rock, AR

Jun 2023 – May 2024
  • ▹ Developed and fine-tuned predictive risk models using gradient-boosted decision trees, analyzing healthcare data to identify high-risk patients and improve early intervention strategies.
  • ▹ Engineered and processed healthcare data features, integrating over 200 variables, including demographic, clinical, and social determinants, to enhance model accuracy and robustness.
  • ▹ Implemented techniques to address class imbalance, utilizing methods like Synthetic Minority Oversampling Technique (SMOTE) to improve model performance in rare outcome predictions.
  • ▹ Assisted in deploying ML models on Azure cloud infrastructure, leveraging Azure Machine Learning for training and Azure Functions for real-time inference to ensure scalable and efficient deployment.
  • ▹ Contributed to model monitoring and governance, integrating SHAP values for explainability and participating in fairness audits to align with AI transparency and compliance standards.
TensorFlow PyTorch Keras Azure Event Hub Stream Analytics

Machine Learning Engineer

Karnataka Bank, Bangalore, India

Apr 2021 – Jun 2022
  • ▹ Assisted in developing and fine-tuning credit risk models using gradient boosting machines (GBM), contributing to an 82% improvement in default prediction accuracy through feature engineering and hyperparameter tuning.
  • ▹ Supported the implementation of ensemble learning techniques for underwriting automation, helping integrate financial ratios, transaction patterns, and alternative data to enhance credit risk assessment.
  • ▹ Contributed to building predictive analytics models for customer acquisition, working with decision trees and logistic regression, leading to a 28% increase in loan approvals while optimizing risk control.
  • ▹ Helped engineer real-time data pipelines using Apache Spark, assisting in processing over 2 million daily transactions to generate feature sets for fraud detection and customer segmentation.
  • ▹ Evaluated model performance and reliability, contributing to back-testing, drift detection, and performance analysis to improve transparency and ensure regulatory compliance.
TensorFlow PyTorch Hadoop Apache Spark Kafka

Machine Learning Engineer

Liberty Insurance, Bangalore, India

May 2020 – Mar 2021
  • ▹ Developed and deployed time-series forecasting models using ARIMA, SARIMA, and Prophet to predict claim frequency and customer acquisition trends.
  • ▹ Built real-time data pipelines for fraud detection and claims processing using Apache Kafka and Apache Spark, ensuring low-latency data processing from multiple sources.
  • ▹ Designed and containerized machine learning models using Flask and Docker for deployment, orchestrating services with Kubernetes for scalable and high-availability systems.
  • ▹ Integrated data warehousing solutions with Snowflake for real-time analytics and optimized data integration, enabling faster query processing for business intelligence teams.
ARIMA SARIMA Prophet Kafka Spark

< Featured Projects />

View Code

Skin Care AI Chatbot

Advanced dermatology chatbot leveraging OpenBioLLM and Pali-Gemma for accurate skin disease diagnosis and personalized treatment recommendations. Features real-time image analysis and medical knowledge graph integration.

Python FastAPI OpenBioLLM Pali-Gemma PyTorch
Live Demo

P2P Community Platform

Developed a FastAPI-based backend for ridesharing, service requests, and rental listings with real-time WebSockets for notifications.Implemented advanced search and routing functions, enabling users to combine multiple services and manage accounts via IVR using Twilio and groq.Designed a dynamic, responsive interface using HTML, CSS, and JavaScript for seamless user interactions

Python FastAPI React MongoDB Google Maps API
View Code

RAG Based Chatbot

Intelligent webpage chatbot utilizing Retrieval-Augmented Generation with ChromaDB for efficient document querying. Features semantic search, context-aware responses, and real-time information retrieval from dynamic content.

Python FastAPI ChromaDB LangChain Llama 2
View Code

Heart Sound Classification

Advanced deep learning system using RNN architecture to classify heart sounds into normal, murmur, and artifact categories. Implements signal processing techniques and achieves 95% accuracy in real-world clinical settings.

Python TensorFlow Keras Librosa NumPy

Ferrell Gas Service Center Clustering

Interactive analytics dashboard with real-time performance metrics visualization. Implements K-Means clustering for service center categorization and features an AI chatbot for data insights using BERT-based models.

Python Dash Plotly scikit-learn BERT PostgreSQL

American Sign Language Recognition

Developed an ASL recognition system using deep learning. Collected and labeled the ASL Alphabet Dataset from Kaggle, adding bounding box annotations with LabelImg. Used transfer learning with ResNet and MobileNet, achieving 92% and 86.76% accuracy, respectively, with GPU acceleration (RTX 2060). Implemented using TensorFlow. The system supports static ASL signs but does not detect letters ‘J’ and ‘Z’ due to motion-based gestures.

Python TensorFlow LabelImg ResNet MobileNet GPU Acceleration

< Education & Achievements />

Education

Master of Science in Data Science

Wichita State University, Wichita, KS

May 2024 | GPA: 3.8/4

Certifications

Awards

Publications