Mani Sai Deeraj

Manisai Deeraj Marru MA, USA |-| +1 - | linkedin.com/in/mani-sai-deeraj Professional Summary Full Stack and Data Engineer with 6+ years of experience building scalable backend services, ML-integrated dashboards, and cloud-deployed applications across startups and enterprises. Adept in Java, Spring Boot, Angular, and Python, with hands-on contributions to AI-driven products in finance, logistics, and healthcare analytics. Skilled in designing end-to-end systems, integrating machine learning models, automating infrastructure with Docker and GitHub Actions, and driving measurable impact in data reliability, system performance, and business insights. Recognized as a 3-time Star Performer -) at TCS with consistent 5-star performance ratings. Technical Skills Frontend: Angular, React, TypeScript, JavaScript, HTML, CSS, Bootstrap, SASS Backend: Java, Spring Boot, JPA, Node.js, Kafka, REST APIs DevOps & Cloud: AWS, Docker, Kubernetes, GitHub Actions, Jenkins, Bamboo Database: PostgreSQL, Oracle, Redis, IBM DB2 Testing: JUnit, Selenium, Postman, Playwright Tools & Practices: Git, Bitbucket, SonarQube, Microservices Architecture, CI/CD Pipelines, Spring Batch, Eureka, Zuul Analytics & ML Tools: Python, Pandas, Tableau, Power BI, SPSS Professional Experience Software Engineer Prowess IT Solutions LLC, Worcester, MA Apr 2025 – Present  Developing internal analytics and automation tools using Spring Boot, Angular, and Python to improve client insights and reporting accuracy across internal business units.  Designing a secure authentication and access control system with JWT and deployed on AWS EC2 using Docker and GitHub Actions, ensuring high uptime and zero-downtime deployments.   Building automated Tableau dashboards integrated with Python pipelines to replace Excel-based reporting, reducing manual work by 20% and improving executive visibility. Worked in a 2-person Agile-lite team, collaborating closely with the founder to prioritize high-impact features, gather client feedback, and rapidly iterate on product requirements. Software Engineer Zangle Technologies Inc, Allen, TX Aug 2024 – Apr 2025  Designed and delivered core modules for a financial dashboard product using Angular 7 and Spring Boot, targeting early-stage investors and wealth managers.  Integrated a basic Flask-based ML prediction API into the dashboard to score risk levels of user portfolios in realtime, enhancing decision-making capability for clients.  Set up JWT-based role-based access control for admin and client views, improving security and reducing unauthorized access issues by 30%.  Deployed and tested builds in AWS staging environments using Docker and Git, and participated directly in Clevel product calls to align AI prototypes with MVP launch goals. Data Science Intern American Airlines Jan 2023 - June 2023  Extracted and cleaned 72,000+ historical flight and baggage logs using SQL and Pandas, preparing structured datasets for analysis.  Conducted exploratory data analysis in Python (Matplotlib, Seaborn) to identify volume spikes across routes, time zones, and aircraft types.  Engineered temporal and categorical features to train forecasting models in SPSS and scikit-learn, achieving >85% accuracy on baggage volume prediction.  Built and deployed Tableau dashboards connected to airline scheduling systems, enabling real-time baggage load monitoring and reducing overstaffing and carousel congestion by 18%. Software Developer Tata Consultancy Services (TCS), Hyderabad March 2019 – Mar 2023 Client: Northern Trust Bank (USA)  Migrated 10+ legacy investment management services from .NET/WebLogic to Spring Boot microservices using Eureka and Zuul, reducing system downtime by 30% and enabling cloud-native, analytics-ready architecture.  Developed Spring Batch ETL pipelines to process 500K+ equity transaction records/month, with enhancements to support downstream AI-driven anomaly detection and reconciliation insights.  Built secure RESTful APIs for portfolio summaries, account access, and advisor dashboards using OAuth2, Spring Security, and integrated Python-based fraud flags into the API layer to improve transparency and response time.  Engineered UI components in Angular and React to visualize client metrics, transaction trends, and ML-derived indicators in operational dashboards.  Containerized microservices and batch jobs using Docker, streamlining CI/CD workflows across environments and reducing onboarding time for ML model integration.  Integrated Bamboo CI/CD, SonarQube, and JUnit into release pipelines, achieving 98% test coverage and reducing deployment cycles from 60 to 20 minutes.  Refactored monolithic equity reporting modules into reusable services that supported predictive analytics via external model endpoints.  Created a PoC on Crystal Reports to restore Power BI functionality post-migration, resolving service-level data misalignment and documenting the workflow for future ML-driven reporting compatibility.  Collaborated with data science and reporting teams to align backend APIs with evolving fraud detection models, supporting compliance teams with traceable model output integration.  Mentored junior developers on microservices architecture, test automation, and best practices for building analytics-compatible Java applications. Software Developer Global Tree Careers Pvt Ltd Sep 2018 – Feb 2019  Built backend APIs in Java to support student engagement and admissions modules, integrating real-time inquiry submission forms to improve lead management.    Applied Hibernate and SQL query optimization techniques to reduce backend processing time, leading to a 22% improvement in page load speeds. Integrated third-party SMS and email services to send real-time status updates to students and internal counselors, enhancing user experience and reducing follow-ups. Collaborated with academic counselors to scope and document key functional requirements, minimizing lastminute changes and aligning tech delivery with business expectations. Software Developer Cognizant Technology Solutions Jan 2018 – Aug 2018  Developed Java modules for a logistics management platform used by regional warehouse clients to track and assign shipments automatically based on predefined business rules.  Refactored legacy SQL procedures to improve response times across the shipment and inventory tracking system, directly contributing to faster logistics reports.  Contributed to QA and UAT efforts across 3 release cycles, ensuring bug-free deployments by creating and running functional test cases.  Explored feasibility of integrating ML-based route optimization by drafting internal documentation and collaborating with senior engineers on data readiness.  Created scripted infrastructure setup procedures to automate repetitive deployment steps, reducing setup time by 20%. Projects Mental Health Prediction in Pandemics – University of North Texas  Designed an ML-driven system to predict mental health deterioration during the COVID-19 pandemic using CDC Behavioral Risk Factor Surveillance Survey (BRFSS) data.  Cleaned and preprocessed state-wise demographic and mental health indicators using Python (Pandas, NumPy) to enable temporal modeling.  Built and compared LSTM and Random Forest models to capture short- and long-term patterns in self-reported symptoms across age and location groups. Deployed Tableau dashboards to visualize high-risk clusters, supporting targeted mental health interventions with 85%+ model accuracy. 🔗 GitHub: github.com/manisaideeraj/mental-health-prediction-docs   Brain Tumor Detection via MRI Classification – University of North Texas      Built an AI solution to assist radiologists by classifying MRI scans into tumor/no-tumor categories using Convolutional Neural Networks (CNNs). Used OpenCV for image preprocessing and trained a VGG16-based model with TensorFlow, achieving 99.2% classification accuracy on validation data. Developed a Flask-based web interface that allowed doctors to upload images and receive instant model predictions, simulating real-world application. Reduced the time for preliminary diagnosis by enabling AI-assisted triage of MRI scans in low-resource clinical setups. 🔗 GitHub: github.com/manisaideeraj/brain-tumor-detection-mri Education M.S. in Advanced Data Analytics, University of North Texas – 2024 MCA, SASTRA University – 2022 B.Sc. in Mathematics, Statistics & Computer Science, Osmania University – 2018