Swar Parekh

I am an Applied AI and Data Engineer with a Master’s degree in Artificial Intelligence from FAU Erlangen-Nürnberg (Germany). I specialize in bridging the gap between raw data pipelines, advanced automation, and intelligent systems—leveraging everything from traditional machine learning to modern Large Language Models (LLMs).

My passion lies in applied AI: engineering intelligent data workflows, extracting features from complex signals, and integrating generative AI tools (such as Claude and OpenAI APIs) to build smart, context-aware automation applications.

Key Technical & AI Achievements:

• Generative AI & Intelligent Automation: Experienced in building Applied AI workflows by integrating state-of-the-art LLMs (Claude, OpenAI) and code-generation models (OpenAI Codex) to automate complex decision-making, parse unstructured data, and generate dynamic programmatic solutions.

• Predictive Maintenance & Fault Detection: For my Master’s thesis at Siemens Energy, I designed an advanced sensor validation framework. I integrated previously ignored critical sensor error data via Modbus into a cloud platform, building an FMEA framework to isolate sensor anomalies and drastically reduce false positives in asset health diagnostics.

• Advanced Signal Processing & Feature Engineering: Developed pipelines in Python and MATLAB to analyze high-dimensional biometric and intracortical brain signals. Utilized advanced preprocessing, filtering, and Principal Component Analysis (PCA) for robust feature extraction.

• Core AI & Enterprise Automation: Implemented core AI frameworks (search, adversarial search, and planning algorithms) in Python. Scaled enterprise-level automation pipelines utilizing conditional keyword-parsing and NLP logic to completely eliminate manual triage overhead.

My Professional Interests: I am seeking roles in Applied AI Engineering, Machine Learning Engineering (Data/MLOps), or Intelligent Workflow Automation. I thrive in environments where I can leverage Python, modern LLM APIs, and cloud infrastructure to build scalable, data-driven solutions that solve tangible operational challenges.