AI Engineer with less than a year in Industrial Automation and Railway Systems.
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ECE graduate passionate about AI/ML applications in Industrial Automation and Railway Systems. Strengthened core concepts in Electronics, Signals & Systems, Embedded Systems, and Control Systems. Built predictive maintenance and privacy-preserving ML models. Hands-on experience in Signaling Interlocking Plans (SIP) using AutoCAD and SCADA systems. Proficient in Python, SQL, MATLAB/Simulink, and machine learning frameworks.
Gati Shakti Vishwavidyalaya (GSV)
Bachelor of Technology · Electronics and Communication Engineering
August 1, 2021 – June 30, 2025
SKP Vidya Vihar
Senior Secondary (Class XII) · PCM
N/A – May 31, 2019
GarudaUAV
Signals & Telecommunications (S&T) Design Intern
January 1, 2025 – May 1, 2025
Noida, Uttar Pradesh, India
Delhi Metro Rail Corporation (DMRC)
Technical Engineering Intern
May 1, 2024 – July 1, 2024
New Delhi, Delhi, India
Indian Oil Corporation Limited (IOCL)
Industrial Automation Intern
May 1, 2023 – June 1, 2023
New Delhi, Delhi, India
Railway Signaling Analysis & Anomaly Detection
June 19, 2026 – Present
Exploring ML-based fault detection in signaling systems by combining domain knowledge of SIP with signal processing techniques.
Federated Learning with Homomorphic Encryption
September 1, 2024 – November 1, 2024
Implemented privacy-preserving decentralized ML framework for collaborative training across distributed nodes. Relevant for secure industrial and railway applications.
Predictive Maintenance for Industrial Equipment
May 1, 2023 – June 1, 2023
Developing ML models (XGBoost + LSTM) on NASA Turbofan dataset to predict Remaining Useful Life (RUL) of machinery. Leveraging concepts from Control Systems & Signals & Systems (GATE preparation).
SQL From A to Z (MySQL)
Udemy
July 1, 2025 – Present
Applications of AI for Predictive Maintenance
NVIDIA
September 1, 2024 – Present
Fundamentals of Deep Learning
NVIDIA
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's academic projects and internships show a strong alignment with industrial and railway applications, which suggests a good cultural fit for organizations focused on these domains. The diversity of projects (predictive maintenance, federated learning, anomaly detection) and exposure to various tools (AutoCAD, SCADA, MATLAB/Simulink) indicate adaptability and a broad technical interest. The certifications from NVIDIA further demonstrate a commitment to continuous learning in AI/ML.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-disciplinary problems. The academic projects and internships suggest a proactive approach to learning and applying technical skills. The focus on privacy-preserving ML (Federated Learning with Homomorphic Encryption) demonstrates an awareness of critical industry concerns. However, without direct assessment data on soft skills, a definitive evaluation of operational fit beyond technical aptitude is limited.