Digital Technology & Management Student · Weiden, Germany
From anomaly detection in machine sensor data through real-time IIoT monitoring to production-ready MLOps pipelines. I turn messy signals into models that ship — with Python, PyTorch, MLflow and Docker.
class Engineer:
name = "Abhi Faldu"
role = "AI / ML"
focus = ["MLOps", "IIoT",
"Anomaly Detection"]
stack = ["PyTorch", "MLflow",
"FastAPI", "Docker"]
location = "Weiden, DE"
def status(self):
return "Open to AI & Data roles"01 — About
I'm a Digital Technology and Management student at OTH Amberg-Weiden, Weiden campus — an English-taught programme I moved into after six semesters of Applied Artificial Intelligence at TH Rosenheim. It pairs the engineering and IT depth I already have with the product and management side of industrial technology.
My hands-on work spans the full lifecycle of industrial AI — from anomaly detection in machine sensor data, through real-time IIoT monitoring, to production-ready MLOps pipelines. I like problems where the data is messy, the constraints are real, and the model has to actually run in production: experiment tracking, drift monitoring, tests, clean deployment — not just a notebook that scores well once.
Before Germany, I worked as a Junior Data Analyst in India, building Power BI dashboards and EDA pipelines. I'm ready to contribute practical solutions in an AI or data science team from day one.
02 — Skills
03 — Projects
Production-grade MLOps workflow for a machine-failure classifier (AI4I 2020, ~10,000 sensor records): training, experiment tracking and a model registry in MLflow, with automatic champion promotion.
Team project (3 people): a MobileNetV2 model, transfer-learned on TrashNet (6 classes), that sorts waste live on a Google Coral Dev Board — no cloud, everything on the device.
An Industry 4.0 simulation: 3 robot arms stream sensor data (temperature, vibration, motor current) over MQTT every second, with real-time anomaly detection via a sliding-window Z-score.
An LSTM autoencoder (PyTorch) for anomaly detection in bearing vibration data (NASA IMS Bearing dataset), catching failures early.
04 — Experience & Education
Weiden in der Oberpfalz, Germany
Reliable work in a high-volume retail environment while studying full-time — strong time management and resilience — while building professional German and intercultural teamwork. Transferred from the Prien am Chiemsee store to Weiden in 10/2026, alongside the move to OTH Amberg-Weiden.
OTH Amberg-Weiden, Weiden Campus, Germany
1st semester of a seven-semester, English-taught programme. Focus: mathematics & informatics, digital technology (IoT, sensors, communication), management & industrial engineering, with an integrated practical semester.
Technische Hochschule Rosenheim, Germany
Six semesters completed before transferring. Focus: Machine Learning, Deep Learning, Neural Networks, Data Science, Database Systems, Software Engineering, IT Security.
Rajkot, India
End-to-end EDA and data-preprocessing pipelines, Power BI dashboards for KPIs, and support on ML model evaluation — translating business requirements into analytical insight.
Atmiya Institute of Technology & Science, Rajkot, India
Completed two semesters of foundational engineering studies.
05 — Contact
I'm open to Werkstudent, Praktikum and entry-level AI / Data roles. The fastest way to reach me is email.