Vamshi Vijayakrishna

Full Stack Engineer | Intl. Software Systems Science, University of Bamberg

💻 Core Technical Competencies

Frontend & UI: TypeScript, JavaScript, WPF (XAML), HTML5, CSS3, Angular
Backend & APIs: C#, ASP.NET Core, Python (Flask), REST API, MySQL
AI & Data Science: LLMs, Data Processing (Pandas, Numpy), Machine Learning Prototypes

Context: Enterprise UI project developed during my tenure at Brose, Hallstadt.

Key Features: Developed and optimized features for C#.NET WPF applications, implementing UI improvements for a better user experience. Designed a unified dashboard to centralize various internal apps and tools into one highly accessible point of access for employees.
The Process: Established a continuous feedback loop with users during development. I actively iterated on the design based on their input to ensure practical feasibility and to overcome natural user resistance to adopting new digital workflows.
Relevance to Thesis: Highlights my capability to design interfaces grounded in real user needs, managing iterative feedback loops directly analogous to translating citizen inputs in a participation process.

Context: End-to-end AI workflow transforming unstructured human feedback into structured insights.

Key Features: Engineered an end-to-end Retrieval-Augmented Generation (RAG) pipeline using the Gemma 2:2B Large Language Model to synthesize complex customer feedback into aspect-aware summaries.
Architecture: Implemented FAISS-based semantic search for accurate context retrieval and integrated sentiment analysis to evaluate and refine the GenAl outputs.
Team Leadership: Coordinated a four-person development team, effectively dividing architecture tasks and ensuring balanced contributions across the entire project lifecycle.
Relevance to Thesis: Proves foundational expertise in building AI-assisted NLP workflows capable of analyzing, retrieving, and summarizing large volumes of unstructured text—a critical skill for processing qualitative citizen input.

Context: Physical hardware system designed for real-time environmental monitoring and remote operations.

Key Features: Designed and built a small, all-terrain mobile robot equipped with a camera, sensors, and a Raspberry Pi computer.
AI Integration: Implemented edge-computing computer vision using the OpenCV (cv2) library in Python to detect and capture human faces, saving them autonomously to a designated directory.
Relevance to Thesis: Demonstrates practical, hands-on experience with physical computing interfaces, remote-controlled microcontrollers, and deploying real-time AI capabilities to hardware.

Context: Full-stack web application designed for predictive resource management.

Key Features: Designed a Flask-based web platform with Al-powered features, including auto-order placement, weekly demand prediction, and seasonal trend analysis.
Impact: Optimized inventory processes for reduced operational costs, minimized stockouts, and improved overall revenue tracking.
Tech Stack: Flask, Python, MySQL, JavaScript, Pandas, NumPy, Keras.