Full-Stack .NET Developer with Angular Expertise

SREE SAI BINDU DEVALAM

San Jose, CA +1-551-***-**** ************@*****.***

PROFESSIONAL SUMMARY

Full Stack .NET Developer with 3+ years of experience delivering enterprise web applications using Angular, ASP.NET Core Web APIs, and SQL Server. Specialized in application modernization, API performance optimization, and secure authentication workflows across multiple business domains. Proven expertise in reducing API latency, improving page load times, and maintaining high test coverage through clean, maintainable code and CI/CD automation. Collaborative team member in Agile environments focused on delivering scalable, role-based solutions aligned with business objectives. TECHNICAL SKILLS

Frontend Development: Angular (v14-v18), Angular CLI, TypeScript, JavaScript (ES6+), RxJS, Observables, Angular Routing & Route Guards, Reactive Forms, Single Page Applications (SPA), Angular Services, HTTP Client, Change Detection Strategy, Responsive UI Development, HTML5, CSS3, SCSS. Backend Development: C#, .NET 6+, ASP.NET Core Web APIs, RESTful Services, Entity Framework Core, LINQ, Dependency Injection, Middleware, Exception Handling, API Versioning, Authentication & Authorization (JWT, Role-Based Access Control), DTOs, Async/Await, Claims-Based Authorization, CORS Configuration, HTTPS Enforcement.

Database & Data Layer: SQL Server, T-SQL, Stored Procedures, Query Optimization, Indexing, Joins, Transactions, Data Modeling. DevOps & Tooling: Azure DevOps, CI/CD Pipelines, Build & Release Automation, Git. Testing & Engineering Practices: xUnit, Moq, Unit Testing, Integration Testing, API Testing (Postman), Swagger/OpenAPI, Secure API Development, Agile/Scrum, Clean Code, SOLID Principles, Design Patterns, N-Tier Architecture. PROFESSIONAL EXPERIENCE

Full Stack .NET Developer Aug 2024 - Present

Datics Inc San Jose, CA

• Developed an enterprise internal operations platform using Angular v18 user interfaces and .NET 6 Web APIs, enabling secure user management, transaction processing, reporting dashboards, and third-party system integrations for internal business teams.

• Modernized page performance by restructuring the frontend using Angular standalone components and lazy loading, reducing initial page load time from ~4.1s to ~2.9s and allowing faster rollout of new features across sprint releases.

• Accelerated backend response consistency by refactoring C# service logic and optimizing Entity Framework Core queries, reducing average API response times by 35% during peak operational usage.

• Enhanced transaction throughput by refining SQL Server stored procedures and indexing strategies, improving high-volume processing capacity by an additional 2K+ transactions per hour in production.

• Fortified release quality by expanding xUnit test coverage beyond 90% and supporting Azure DevOps CI/CD pipelines, reducing post-release defects by 40% per release cycle across deployments.

• Strengthened release stability by standardizing Git workflows including feature branching, pull requests, and peer code reviews, integrated with Azure DevOps CI/CD pipelines to reduce integration issues across sprint deployments.

• Hardened API security through centralized exception handling, JWT-based authorization, CORS, and HTTPS in a multi-tier .NET Web API architecture, reducing authorization-related production issues by 25%. Full Stack Developer Jan 2021 - Aug 2022

Cognizant India

• Delivered enterprise web application functionality supporting U.S.-based business operations by developing Angular front-end features and ASP.NET Core Web APIs backed by SQL Server.

• Boosted user-facing performance by reducing UI-related support tickets by ~20% through responsive Angular UI optimizations and efficient REST API integrations, improving overall page responsiveness.

• Stabilized application behaviour by cutting production issue diagnosis time from 2 days to under 1.5 days through structured API error handling and request/response validation in .NET services.

• Accelerated reporting turnaround by reducing report execution time by 10+ seconds per run through optimization of SQL Server stored procedures and complex queries, resulting in faster data retrieval and improved report availability for operational teams.

• Improved release predictability by enabling consistent on-time sprint deliveries through disciplined Git-based source control and close collaboration with onshore product and QA teams.

PROJECTS

IntelliSuggest: AI-Powered Context-Aware Recommendation Platform (Angular 20, ASP.NET Core 8, Genkit)

• Architected a Generative AI full-stack application using Angular 20 and ASP.NET Core Web APIs, integrating Large Language Models (LLMs) via Google Genkit and Gemini to deliver context-aware recommendations through a scalable client-server architecture.

• Reinforced AI recommendation quality and consistency by implementing structured prompt engineering, schema-validated DTOs, and centralized exception handling in .NET, resulting in predictable and reliable AI responses.

• Streamlined real-time AI user interactions by reducing UI latency during AI inference with Angular Signals and optimized change detection.

• Enhanced AI system scalability and performance by enabling asynchronous, non-blocking AI request handling in ASP.NET Core and optimized data access, maintaining sub-500ms AI response times under load. EDUCATION

Master of Science in Computer Science Aug 2022 - May 2024 University of Maryland, Baltimore County Baltimore, MD CERTIFICATIONS

• AWS Certified Cloud Practitioner

• Prompt Engineering for Developers – DeepLearning.AI

• Google Generative AI Learning Path - Google Cloud PUBLICATIONS

Post-Roe Public Discourse: A Temporal Analysis of Discussion on US Abortion Law Changes ACM Digital Library

• Executed large-scale NLP and temporal analysis on 189K+ Reddit and YouTube comments to examine shifts in public discourse following the overturn of Roe v. Wade.

• Engineered an end-to-end text-mining pipeline using Python and NLP libraries including SBERT, scikit-learn, and ConceptNet to perform semantic similarity, thematic classification, and opinion mining.

• Eliminated data noise by 88% through advanced text preprocessing techniques, improving embedding quality and accuracy of theme detection.

• Analyzed shifts in social, legal, ethical, and political discourse by applying temporal segmentation across immediate, peak-debate, and long-term phases, supported by research-grade visualizations using Matplotlib and Seaborn.

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