Signal Processing / AI Engineer

Job Summary

The Signal Processing / AI Engineer will be responsible for developing advanced signal processing algorithms, machine learning models, and predictive analytics for industrial condition monitoring solutions. The role focuses on extracting meaningful insights from vibration, acoustic, electrical, thermal, and process data to support fault detection, diagnostics, predictive maintenance, and performance optimization for MSCOPE, Portable & Universal Motor-Cum-Pump Performance Monitor (PU-MPPM), and Power Quality Analyzer (PQA).

Key Responsibilities

Signal Processing

• Develop digital signal processing (DSP) algorithms for vibration, acoustic, current, voltage, and temperature signals.
• Design algorithms for signal conditioning, filtering, denoising, and feature extraction.
• Develop and optimize Fast Fourier Transform (FFT) and spectral analysis algorithms.
• Perform time-domain, frequency-domain, and time-frequency analysis.
• Develop envelope analysis, cepstrum analysis, wavelet transform, and order tracking algorithms.
• Implement digital filters (FIR, IIR, Butterworth, Chebyshev, Kalman Filters).
• Optimize algorithms for real-time embedded implementation.

AI & Machine Learning

• Develop AI/ML models for predictive maintenance and condition monitoring.
• Build models for fault detection, fault classification, anomaly detection, and Remaining Useful Life (RUL) estimation.
• Develop supervised, unsupervised, and semi-supervised learning models.
• Optimize AI models for deployment on edge devices.
• Develop digital twin and predictive analytics models for industrial assets.
• Validate and continuously improve AI model performance using field data.

Condition Monitoring & Diagnostics

Develop algorithms for:

• Bearing fault detection
• Motor fault diagnostics
• Pump performance degradation
• Cavitation detection
• Shaft misalignment
• Mechanical looseness
• Rotor imbalance
• Gearbox fault detection
• Electrical fault diagnosis
• Power quality event detection
• Thermal anomaly detection
• Pump efficiency estimation
• Health Index (HI) calculation
• Remaining Useful Life (RUL) prediction

Data Analytics

• Design data preprocessing pipelines.
• Perform feature engineering and feature selection.
• Develop statistical analysis and trend monitoring algorithms.
• Analyze large industrial datasets from field deployments.
• Generate insights and performance reports.

Embedded AI

• Optimize AI models for embedded Linux and MCU-based platforms.
• Support deployment on ARM-based edge gateways and embedded systems.
• Optimize inference performance for low-power devices.
• Work with hardware and firmware teams for real-time implementation.

Software Development

• Develop reusable signal processing and AI libraries.
• Create APIs and SDKs for algorithm integration.
• Maintain algorithm documentation and version control.
• Support cloud analytics and dashboard integration.

Technical Skills

Signal Processing

• Digital Signal Processing (DSP)
• FFT and spectral analysis
• Wavelet Transform
• Envelope Analysis
• Cepstrum Analysis
• Time-frequency analysis
• Digital filter design (FIR/IIR)
• Statistical signal analysis
• Vibration and acoustic signal processing

Artificial Intelligence & Machine Learning

• Python
• TensorFlow
• PyTorch
• Scikit-learn
• XGBoost
• Neural Networks
• CNN, RNN, LSTM
• Autoencoders
• Anomaly Detection
• Predictive Analytics
• Time-series forecasting

Programming

• Python
• C/C++
• MATLAB
• NumPy
• SciPy
• Pandas
• OpenCV (preferred)
• SQL (preferred)

Industrial Knowledge

• Vibration monitoring
• Motor diagnostics
• Pump performance analysis
• Acoustic emission analysis
• Power quality monitoring
• Industrial IoT
• Predictive maintenance
• Condition monitoring

Communication & Integration

• MQTT
• OPC UA
• Modbus RTU/TCP
• REST APIs
• Edge computing architectures

Software & Development Tools

• Azure DevOps for backlog management, sprint planning, task tracking, and release planning.
• Git for source code management, branching, pull requests, and version control.
• Jupyter Notebook for research and model development.
• Docker (preferred) for deployment and testing.
• Linux development environment.

Preferred Experience

• Industrial AI applications
• Edge AI deployment
• Embedded Linux systems
• Motor and pump diagnostics
• Industrial sensor fusion
• Predictive maintenance platforms
• Time-series database technologies
• Cloud-based analytics platforms

Quality & Information Security

• Follow ISO 9001:2015 Quality Management System (QMS) procedures throughout product development.
• Ensure compliance with ISO/IEC 27001 Information Security Management System (ISMS) requirements.
• Maintain engineering documentation under document control.
• Follow secure coding, version control, backup, and change management practices.
• Support internal and external quality and information security audits.

Agile & DevOps

• Participate in Agile Scrum ceremonies, including sprint planning, daily stand-ups, sprint reviews, and retrospectives.
• Manage development activities using Azure DevOps Boards and Repositories.
• Collaborate using Git workflows, code reviews, and pull requests.
• Support CI/CD practices for algorithm testing and deployment.

Required Qualifications

• Bachelor's or Master's degree in Electronics, Electrical Engineering, Computer Science, Artificial Intelligence, Data Science, Signal Processing, or a related field.
• 2-5 years of experience in Digital Signal Processing, Machine Learning, or Industrial AI.
• Experience developing industrial condition monitoring or predictive maintenance solutions is preferred.