Sridhar Reddy Singolla

Key Skills

Software

Mindrift

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

Classification

Freelancer Overview

I am passionate about AI and machine learning, with hands-on experience in developing NLP-powered chatbots, AI-driven resume screening systems, and multilingual conversational platforms. My background includes building full-stack applications that leverage data annotation, prompt engineering, and semantic search using tools like LangChain, OpenAI API, Pinecone, and FAISS. I am skilled in Python, PyTorch, TensorFlow, and have a strong understanding of data structures, algorithms, and backend development. Through internships and hackathons, I have worked on projects involving data labeling for NLP tasks, chatbot training, and user interaction workflows, always focusing on data quality and continuous improvement. My adaptability, problem-solving abilities, and commitment to learning make me eager to contribute to data annotation and AI training data roles.

Labeling Experience

AI & Machine Learning Intern

MindriftComputer Code ProgrammingClassification

As an AI & Machine Learning Intern at L&T, I played a role in developing a real-time fraud detection system that involved automated fraud classification with machine learning. I implemented fraud detection and monitoring systems, which required training classification models using transaction data to differentiate between legitimate and fraudulent cases. My responsibilities included continuous evaluation of model outputs and refining classification logic for optimal detection accuracy. • Utilized Apache Spark and Random Forest to process and label financial transaction data. • Set up real-time streaming and automated ingestion using Kafka and Streamlit dashboards. • Focused on classifying text-based transaction logs for anomaly detection tasks. • Supported model improvement through iterative re-labeling and evaluation processes.

As an AI & Machine Learning Intern at L&T, I played a role in developing a real-time fraud detection system that involved automated fraud classification with machine learning. I implemented fraud detection and monitoring systems, which required training classification models using transaction data to differentiate between legitimate and fraudulent cases. My responsibilities included continuous evaluation of model outputs and refining classification logic for optimal detection accuracy. • Utilized Apache Spark and Random Forest to process and label financial transaction data. • Set up real-time streaming and automated ingestion using Kafka and Streamlit dashboards. • Focused on classifying text-based transaction logs for anomaly detection tasks. • Supported model improvement through iterative re-labeling and evaluation processes.

2024 - 2025

Education

Work History