AI Systems Engineer | Machine Learning Infrastructure | Predictive Maintenance | Data Pipelines
I build AI and data systems that turn complex operational data into practical decision tools.
My work focuses on anomaly detection, predictive maintenance, machine learning pipelines, and automated analytics infrastructure.
My background combines hands-on experience working in high-volume automated logistics environments with applied machine learning engineering.
- Predictive Maintenance
- Anomaly Detection
- Machine Learning Systems
- Data Pipelines
- Industrial AI
- Time-Series Analysis
An adaptive predictive maintenance platform designed to reduce false alerts and predict likely equipment failures using sensor behavior, repair history, and technician feedback.
Key capabilities:
- anomaly detection for sensor data
- fault classification
- repair-time estimation
- parts recommendation
- technician feedback learning
Tech Stack: Python, Pandas, Scikit-learn, Time-Series Analysis
Precision-first anomaly detection system built for multivariate sensor data.
Features:
- high-precision anomaly detection
- explainable alerts
- multivariate time-series modeling
ETL pipeline analyzing 4,000+ downtime events to generate reliability insights and MTBF metrics.
Features:
- ETL pipeline design
- reliability analytics
- operational performance metrics
Python
Pandas
NumPy
Scikit-learn
SQL
Machine Learning
Time-Series Analysis
Data Pipelines
Modern automated logistics environments generate large volumes of equipment sensor alerts. However, many alerts are noisy or triggered by temporary operational conditions such as peak system load.
This project explores how machine learning can improve equipment diagnostics by:
• distinguishing real developing failures from operational noise
• learning from technician repair outcomes
• identifying recurring mechanical failure patterns
• predicting repair requirements before maintenance begins
The system architecture combines anomaly detection, fault classification, repair-time estimation, and technician feedback loops to create a practical predictive maintenance intelligence layer.
Sensor Data ↓ Feature Engineering ↓ Anomaly Detection ↓ Fault Classification ↓ Failure Pattern Analysis ↓ Repair Intelligence ↓ Technician Feedback Learning Loop