Rajesh Parishapogu
$ echo "AI Software Engineer || Python • Machine Learning • Flask • SQL || Building AI applications solving real-world problems"
AI Engineering student at Sri Mittapalli College of Engineering with hands-on experience building and deploying end-to-end web applications using Python, Flask, SQL, HTML, and CSS. Developed machine learning projects including Diabetes Prediction, Student Marks Prediction, and an Authentication Login System. Currently strengthening expertise in Software Engineering, Backend Development, Machine Learning, and Data Structures & Algorithms (DSA). Actively seeking internship opportunities in AI Engineering, Backend Development, and Software Development while contributing to real-world projects and continuously expanding technical skills.
skills
projects
Login Authentication System
Login Authentication System A full-stack app using Python, Flask, HTML, and CSS for secure login workflows. Overview Covers user authentication, backend logic, and frontend integration. Key Features - Flask-based login system - Python backend - Responsive HTML/CSS UI Tech Stack Python • Flask • HTML • CSS Key Learning Outcomes - Built an authentication workflow - Improved full-stack skills Start of my journey in Software Engineering and AI.
Student Marks Prediction System
Student Marks Prediction System ML web app that predicts student performance via an interactive interface. Highlights - Predicts student marks. - Built with Python, Flask, HTML, CSS. - User-friendly UI with real-time predictions. Tech Stack Python • Flask • Scikit-learn • Pandas • NumPy • HTML • CSS Learning Outcomes - Built and deployed an ML app. - Experience in preprocessing, training, and prediction. - Integrated ML with Flask.
Diabetes prediction system
Diabetes Prediction System A web app that predicts diabetes risk using patient data. Features - ML-based risk prediction - Built with Python and Flask - Inputs: Glucose, BMI, Blood Pressure, Age, Insulin - Real-time results Tech Stack Python • Flask • Scikit-learn • Pandas • NumPy • HTML • CSS Key Learnings - End-to-end ML workflow - Data preprocessing and model integration - Model deployment with Flask
Cafe Management System
Cafe Management System A Python menu-driven app simulating a cafe ordering system. Key Features - Uses Python with conditionals, dictionaries, and user input. - Calculates bills automatically. - Simulates a basic ordering workflow. Tech Stack Python Key Learning Outcomes - Improved Python fundamentals and problem-solving. - Built a console-based application. Demonstrates practical use of core Python concepts.
FunChat Application
FunChat Application A console-based chat app in Python simulating basic messaging with user management and chat history. Key Features - User registration and messaging - Chat history tracking - Menu-driven interface with validation Tech Stack Python Key Learning Outcomes - Used core Python concepts (functions, loops, conditionals) - Improved problem-solving skills - Built a structured console application
experience
Completed a 45-day AI/ML internship focused on building models with Python and strengthening ML fundamentals through hands-on projects.
Highlights
- Worked with NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn
- Learned data preprocessing, visualization, and ML workflows
- Built a House Price Prediction System
Tech Stack
Python • NumPy • Pandas • Matplotlib • Seaborn • Scikit-learn
Outcomes
- Stronger Python and ML foundation
- Practical experience in data analysis and predictive modeling
- Represent CampusUnite by promoting student-focused programs, events, and career opportunities on campus.
- Connect students with learning resources, internships, workshops, and community initiatives.
- Engage with students to increase awareness and participation in CampusUnite activities.
- Build leadership, communication, networking, and community engagement skills through campus outreach.
- Collaborate with the CampusUnite team to strengthen the student community and create meaningful impact.
- Selected among the Top 10 teams from all participating teams.
- Collaborated with a cross-functional team to design and present an AI-based solution.
- Applied problem-solving, teamwork, and practical implementation skills in a time-constrained environment.
- Strengthened collaboration, critical thinking, and solution design through a competitive innovation challenge.
education
achievements
Completed a 45-day AI/ML Internship focused on developing AI models using Python. The program strengthened my fundamentals in Machine Learning through hands-on learning and project implementation.
Key Highlights
- Completed a 45-day internship in AI & Machine Learning.
- Strengthened Python programming and Machine Learning fundamentals.
- Gained hands-on experience with NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
- Learned data preprocessing, visualization, and basic ML workflows.
- Developed a House Price Prediction System using Python and Machine Learning.
Tech Stack
Python • NumPy • Pandas • Matplotlib • Seaborn • Scikit-learn
Key Learning Outcomes
- Built a strong foundation in Python and Machine Learning.
- Applied AI concepts to solve real-world problems.
- Gained practical experience in data analysis and predictive modeling.
- Strengthened problem-solving and project development skills.
Verification Link :https://drive.google.com/file/d/1J5Vgh4Vpjmj3SjA--tg9eOHaL_j1t705/view?usp=drivesdk
Completed a Software Engineering Job Simulation by JPMorganChase & Co. via Forage, gaining hands-on experience with real-world development workflows.
Highlights
- Completed a virtual simulation.
- Learned real-world software development practices.
- Strengthened backend development understanding.
- Improved problem-solving skills.
Learning Outcomes
- Applied software engineering concepts.
- Enhanced analytical thinking.
- Gained insight into industry processes.
- Reinforced interest in scalable backend and AI systems.
Verification link :https://drive.google.com/file/d/14bQmbNSQQ4Y5u90oAHc-5prEzX4lHuFm/view?usp=drivesdk
Completed the AI Fluency for Students program by Anthropic, gaining practical knowledge on using AI effectively, responsibly, and ethically for learning and problem-solving.
Highlights
- Learned the 4D Framework: Delegation, Description, Discernment, and Diligence.
- Explored AI as a tool for learning, productivity, and career planning.
- Practiced evaluating AI-generated responses critically.
- Strengthened understanding of responsible and ethical AI usage.
Learning Outcomes
- Improved prompt engineering and AI interaction skills.
- Developed critical thinking when working with AI.
- Learned best practices for effective and ethical AI adoption.
- Reinforced my foundation for becoming an AI Software Engineer.
Verification link;https://drive.google.com/file/d/1FNBSumacupVJmJT8dE9csIXSPzt0b4U7/view?usp=drivesdk
Completed Goldman Sachs’ Operations Job Simulation via Forage, gaining hands-on experience in financial operations and trade processing.
Highlights
- Completed a virtual Operations Analyst simulation.
- Analyzed data to identify trade and asset transfer issues.
- Worked with Trading, Compliance, and IT teams.
- Applied risk management and compliance practices.
Learning Outcomes
- Improved analytical and problem-solving skills.
- Gained insight into trade lifecycle and operations.
- Learned cross-functional collaboration.
- Strengthened understanding of risk and compliance.
Verification link ; https://drive.google.com/file/d/1XxEtAOmfh5gy0Dz6La_3RrVgEYaVEs9Q/view?usp=drivesdk