Surendra A.

Key Skills

Software

Don't disclose

OpenCV AI Kit (OAK)

Top Subject Matter

AI agent for data analysis and document-based information extraction (OCR/PDF parsing)

Machine learning training for medical time-series prediction (blood pressure)

Computer vision model development for face detection

Top Data Types

Document

Image

Top Task Types

Data Collection

Fine-tuning

Object Detection

Freelancer Overview

AI Agents Developer Intern — Streamlit-based AI Data Analyst Agent (June 2025 to August 2025). Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose, Apache Spark, and OpenCV AI Kit (OAK). Education includes Bachelor of Technology, Indian Institute of Information Technology Design & Manufacturing, Kurnool (2022) and Higher Secondary Education, Sri Chaitanya College (2022). AI-training focus includes data types such as Document, Computer Code, and Programming and labeling workflows including Data Collection,

Labeling Experience

AI Agents Developer Intern — Streamlit-based AI Data Analyst Agent (June 2025 to August 2025)

Don't discloseDocumentData Collection

Built an AI Data Analyst Agent that supports ingestion and processing of multiple document and data file formats for conversational querying. Implemented OCR and PDF parsing to convert inputs into machine-usable text for downstream analysis. Designed chat-based interaction features to improve accessibility of extracted information.• Supported CSV, Excel, PDF, JSON, and XML inputs for agent workflows• Used OCR and PyPDF2 to extract text from PDFs• Enabled natural-language queries over processed data for analysts• Added persistent chat history and interactive Plotly visualizations for usability

2025 - 2025

Project — Real-Time Face Detection Application (Dec 2024)

OpenCV AI Kit (OAK)ImageObject Detection

Built a computer vision system for real-time face detection across varied lighting and viewing conditions. Optimized detection performance using classic vision methods combined with parallel processing to achieve high throughput. The work focused on training/setting up the detection pipeline to reliably identify faces in video frames.• Achieved 95% detection accuracy across lighting and angle variations• Used Haar cascade classifiers for face detection• Applied multi-threading to process 30 frames per second on standard hardware• Delivered an end-to-end face detection application using Python/OpenCV

2024 - 2024

Project — Blood Pressure Prediction System (March 2024)

Fine-tuning

Developed and trained a machine learning model for early detection of abnormal blood pressure patterns within a real-time health monitoring workflow. Implemented a time-series learning approach using distributed streaming and large-scale data pipelines. Focused on model development and performance to support clinical risk prediction use cases.• Trained an 85% accurate prediction model for abnormal blood pressure early detection• Processed 1000+ health data points per minute using distributed computing techniques• Used time series analysis with machine learning and deep learning tools• Built the platform using Spark/Kafka for continuous data flow

2024 - 2024

Education

Work History