Software Engineering Data Analysis

Sri Haritha Chalicham

**** ******* **, *** ***, Raleigh, NC - 27606

919-***-**** # *******@****.*** ï linkedin.com/in/chalicham-sri-haritha/ Education

North Carolina State University, Raleigh Aug 2023 - May 2025 Master of Computer Science GPA : 3.9/4.0

CourseWork: Design and Analysis of Algorithms, Automated Learning and Data Analysis, Object Oriented Design and Development, Real Time AI and ML systems, Parallel Systems, Computer Networks, Software Engineering, Neural Networks Koneru Lakshmaiah Educational Foundation, Guntur, India June 2016 - May 2020 Bachelor of Technology in Electronics and Communication Engineering CGPA : 8.9/10.0 CourseWork: Computer Architecture and Organization, C Programming and Data Structures, Object Oriented Programming Technical Skills

Programming Languages: Python, Java, Ruby on Rails, SQL, Shell Scripting, C Web Technologies/Frameworks: HTML, CSS, JavaScript, Jinja2, Flask Tools/Technologies: Github, TensorFlow, pyTorch, Excel, JIRA, Confluence, Linux, Matlab, Kubernetes, Docker, Pega AWS: ACM, ECS, S3, ECR, DynamoDB, CLI

Database: MySQL, SQLite, PostgreSQL

Certifications: Pega CSA(v7.4), CSSA(v8), CPDC(v8.5) Professional Experience

Atachi Systems, San Jose, California, USA

AI Application Developer June 2024 - Dec 2024

• Developed scalable backend services using Python and Flask for AI-driven applications, improving system efficiency by 40%.

• Implemented responsive user interfaces using HTML5, CSS3, and JavaScript, enhancing user experience and web application functionality.

• Integrated the OpenAI API to dynamically generate Python scripts for automating equipment data analysis and monitoring, reducing manual effort and increasing operational efficiency.

• Optimized data retrieval in DynamoDB using pagination and indexing, improving query efficiency by 80% and ensuring seamless handling of large datasets.

• Enhanced parallel file processing performance by exploring multi-threading techniques.

• Integrated vector databases for batch file uploads and retrieval, streamlining AI-driven workflows and accelerating OpenAI prompt execution.

• Implemented containerized deployments using Docker and AWS, reducing deployment time by 30%. Infosys Ltd, Hyderabad, India

Associate Consultant Dec 2020 - July 2023

• Implemented a multilevel decisioning model in Pega Customer Decision Hub, optimizing customer engagement by recommending the next best offers.

• Developed REST API for seamless data integration, enhancing application interoperability.

• Led outbound campaign management, analyzing customer interactions to enhance user engagement and sales conversion during inbound calls, contributing to increase in sales.

• Optimized email and SMS workflows, improving reliability and efficiency for seamless campaign execution.

• Supported deployment and release processes using Pega Deployment Manager, reducing deployment time by 40% and minimizing deployment failures by 30% through automation and standardized processes. Projects

Railway Ticketing System Ruby on Rails, Ruby Mine

Designed and developed a fully functional railway ticketing system using Ruby on Rails, leveraging the MVC architecture to ensure scalability and maintainability.

Implemented user authentication and role-based authorization, allowing passengers to securely manage their bookings and view past transactions.

Developed features such as real-time seat availability, ticket booking, and payment processing, enhancing user experience and ensuring efficient ticketing operations.

Information Verification Tool Python, Gemini API, Whisper API, Gradio

Developed an intuitive tool aiding in the verification of online information.

Integrated Google search results for content analysis and leveraged Gemini API to evaluate information reliability.

Implemented text, audio, and video input processing using Whisper API for speech-to-text conversion.

Built a seamless and user-friendly web interface using Gradio, enabling efficient user interaction. Optimization of License Plate Recognition Python

Applied pruning (35% model weight reduction), maintaining 88.5% accuracy.

Used quantization to improve CPU inference speed while preserving accuracy.

Leveraged TVM Relay optimizations (type inference, fusion, autotuning) to boost accuracy to 90.3%.

Reduced inference time from 0.0024s to 0.0008s, enabling real-time deployment. Automated Transcription of Middle English Manuscripts Python

Developed an OCR solution for Middle English manuscripts using Tesseract, Logistic Regression (LR), and Artificial Neural Networks (ANN), effectively addressing noise-related challenges in historical texts.

Enhanced word differentiation by applying DBScan clustering, and proposed custom algorithms to improve OCR accuracy and performance.

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