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SRIKANTH PATANENI

AI Operations Associate • India • s***************@gmail.com • +91*******686 • mca/••••• • drivetube.ai/•••••

Professional Summary

AI Operations Associate with 1 year of experience executing AI data operations, image annotation, and data quality workflows. Experienced in large-scale visual data labeling, product information verification and structured data extraction to produce high-quality training datasets. Strong practical exposure to annotation process design, SOP compliance, and data validation in a target-driven environment, supported by hands-on Python and ML coursework using Pandas, NumPy and Scikit-learn. Delivered responsive web pages and login interfaces using HTML, CSS and JavaScript during web development internships and built ML classification models (Logistic Regression, KNN) for dataset experiments. Seeking an AI Data / ML Data Operations role where I can apply rigorous annotation practices, dataset validation, and entry-level analytics to improve model training data quality and operational efficiency.

Technical Skills

Programming Language: Python,JavaScript,SQL
Frontend Technologies: HTML,CSS,UI Development,Login
Data Analysis & Visualization: Pandas,NumPy,Matplotlib,Data Annotation
Machine Learning & AI: Logistic Regression,K-Nearest Neighbors
AI/ML Frameworks & Libraries: scikit-learn
Compliance & Governance: Data Quality
Process Modeling & Documentation: Standard Operating Procedures

Work Experience

TELEPERFORMANCE GLOBAL SERVICES
Associate, AI Operations / Data Annotation
Nov 2024 – Present
Supported AI operations and dataset preparation for image-based model training; worked on visual annotation, product-data verification and dataset validation tasks.
  • Annotated large batches of product images to generate pixel/box-level labels for supervised model training using defined labeling schemas.
  • Validated product metadata and corrected attribute inconsistencies to maintain dataset integrity for downstream model ingestion.
  • Extracted structured data from image and text sources to produce training records following project-specific extraction rules.
  • Designed and documented quality-control checklists and SOP clarifications to reduce rework and ensure consistent label outputs.
  • Led annotation quality reviews and coached new annotators on labeling conventions and productivity expectations as AI Annotation Lead.
  • Coordinated with project leads to triage ambiguous cases and implement annotation guidelines that improved labeling consistency.
Oasis Info Byte
Web Developer Intern
Oct 2023 – Nov 2023
Contributed to web development tasks focused on responsive UI and user authentication interfaces for client-facing pages.
Tech Stack: HTML, CSS, JavaScript, Login
  • Developed responsive web pages using HTML and CSS to ensure consistent layouts across desktop and mobile screens.
  • Implemented interactive client-side features with JavaScript to improve usability and form validation.
  • Built a user-friendly login interface implementing front-end validation and accessible form controls.
  • Refactored page layouts to improve rendering performance and simplify responsive breakpoint logic.
  • Partnered with designers to translate wireframes into functional UI components and documented styling conventions.
  • Performed cross-browser testing and fixed UI regressions to maintain consistent visual behavior.
AICTC
Python Developer Intern
Sep 2023 – Oct 2023
Built a real-time weather application interface and small utility apps using Python to practice API integration and UI scripting.
Tech Stack: Python
  • Designed and implemented the real-time weather application interface using Python to fetch and display dynamic weather information.
  • Built an interactive to-do list application with persistent state handling and simplified UI controls.
  • Structured application logic to separate data retrieval, formatting, and presentation layers for maintainability.
  • Wrote unit-level checks and simple input validation to improve application robustness during user interactions.
  • Documented application setup and usage steps to streamline handover and replication of development environment.
  • Debugged and optimized Python scripts to reduce response delays and improve user interaction fluidity.
Bharath Intern
Machine Learning Intern
Aug 2023 – Sep 2023
Developed and evaluated machine learning classification models for dataset experiments, including a wine quality prediction project.
Tech Stack: Scikit-learn, Pandas, Matplotlib, SQL, Logistic Regression, K-Nearest Neighbors
  • Developed a wine quality prediction model using Scikit-learn implementing Logistic Regression for baseline classification.
  • Applied K-Nearest Neighbors to compare model performance and demonstrate algorithm trade-offs on the same dataset.
  • Preprocessed data using Pandas to handle missing values, feature scaling and categorical encoding prior to modeling.
  • Explored data distributions and model results using Matplotlib to communicate findings and select model improvements.
  • Queried and prepared datasets with SQL-style data manipulations to create training and validation splits.
  • Documented modeling experiments and hyperparameter choices to support reproducibility of results.

Projects

Wine Quality Prediction
Tools Used: Python, Scikit-learn, Logistic Regression, K-Nearest Neighbors, Pandas, NumPy, Matplotlib
  • Built a classification pipeline in Python to predict wine quality using Logistic Regression as the primary model.
  • Compared performance with a K-Nearest Neighbors classifier to evaluate model selection trade-offs.
  • Performed feature engineering and scaling using Pandas and NumPy to improve model inputs.
  • Visualized model outcomes and feature importance with Matplotlib to guide iteration decisions.
Real-Time Weather Application
Tools Used: Python, UI Development
  • Developed an interactive interface in Python to display real-time weather information and user-friendly elements.
  • Structured the application to separate data retrieval and presentation logic for maintainability.
To-Do List Application
Tools Used: Python, UI Development
  • Implemented a simple task-management application with persistent state and intuitive UI components.
  • Refined user interactions and simplified input validation to improve usability.

Education

Vaagdegi College of Engineering (VCE)
Master of Computer Applications (MCA) • 2025
Capital Degree College
Bachelor of Science (B.Sc.) • 2022
Sri Gayathri Junior College
XII • 2019
NSKK High School
X • 2017

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