SUSHMA SRIRAMULA

Entry-Level Machine Learning Engineer
Hyderabad, IN.

About

Highly motivated Computer Science graduate with over a year of hands-on experience in Machine Learning, specializing in Python, Scikit-learn, and TensorFlow. Proven ability to develop and deploy high-accuracy models, achieving up to 95% in NLP and regression tasks, and proficient in REST APIs, data preprocessing, and Agile methodologies. Seeking an entry-level Machine Learning Engineer role to leverage expertise in delivering innovative, data-driven solutions and contribute to cutting-edge projects.

Work

Accenture (via Forage)
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Job Simulation: Data Analytics & Visualization

Virtual, Global, US

Summary

Analyzed and visualized complex datasets for client scenarios, delivering data-driven insights and structured data workflows.

Highlights

Cleaned and modeled datasets using Excel and Power BI, creating dashboards for 5+ client scenarios.

Delivered data-driven insights through visualizations, improving decision-making by 15% in simulated client presentations.

Built data pipelines for 1,000+ records, ensuring data integrity and consistency.

Presented insights to a virtual team of 6, receiving 90% positive feedback for clarity and impact.

Octanet Services Pvt. Ltd.
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Python Intern

Remote, Global, US

Summary

Developed a secure command-line ATM interface and optimized Python scripts, enhancing system reliability and performance.

Highlights

Built a command-line ATM interface using Python and Git in VS Code, implementing secure PIN validation and error handling.

Streamlined code logic for balance checks and transactions, supporting 100+ test cases with 98% pass rate.

Debugged Python scripts to optimize performance, reducing runtime errors by 20%.

Intrainz Technologies
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Machine Learning Intern

Remote, Global, US

Summary

Developed and deployed a fake news detection system, leveraging machine learning techniques and Agile workflows to enhance real-world news classification.

Highlights

Developed a Fake News Detection system using Logistic Regression and TF-IDF vectorization, achieving 95% accuracy with Scikit-learn and NLTK (Python).

Optimized NLP performance by 10% through hyperparameter tuning, text preprocessing, feature engineering, and model evaluation.

Implemented cross-validation techniques, improving model robustness on a 10,000+ record dataset.

Designed data pipelines for 5,000+ text entries, reducing processing time by 15%.

Collaborated in an Agile team of 4 to deploy models via REST APIs, enhancing real-world news classification.

Volunteer

Jyothishmathi Institute of Technology Sciences
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Student Leader & Mentor

Nalgonda, Telangana, India

Summary

Led technical events and mentored peers, fostering machine learning and career growth within the student community.

Highlights

Organized 3 virtual technical events for 100+ attendees, promoting machine learning concepts and career development.

Mentored 15+ peers in online ML communities, guiding Python and Scikit-learn project development and fostering collaborative learning.

Education

Jyothishmathi Institute of Technology Sciences
Nalgonda, Telangana, India

B.Tech

Computer Science

Grade: 7.3

Courses

Machine Learning

Deep Learning

Linear Algebra

Probability

Data Science

Neural Networks

Languages

English

Skills

Programming

Python, C, SQL.

Machine Learning

Scikit-learn, TensorFlow, Keras, PyTorch, NLTK.

Data Science

Pandas, NumPy, Matplotlib, Seaborn, Feature Engineering.

ML Concepts

Supervised Learning, Unsupervised Learning, CNNs, RNN, NLP, Regression, Classification.

Tools

Jupyter Notebook, Git, VS Code, Power BI, REST APIs.

Soft Skills

Problem-Solving, Team Collaboration, Communication, Time Management.

Projects

Fake News Detector

Summary

Developed a Logistic Regression classifier to detect fake news, incorporating advanced NLP and model deployment techniques.

Retail Price Optimizer

Summary

Developed a Random Forest regression model to predict product prices and enhance stakeholder decision-making.

Mental Health Predictor

Summary

Created a classifier to predict mental health status from survey data, improving reliability through feature engineering and visualizations.

Movie Recommendation System

Summary

Designed and deployed a collaborative filtering recommender system to enhance user experience and scalability.