Senior AI/ML Engineer - Time Series Analysis

WonderBiz Technologies Pvt.; Mumbai; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

WonderBiz Technologies Pvt. seeks a Senior AI/ML Engineer to build and deploy production-grade AI/ML solutions for industrial equipment, focusing on motor and pump systems. You will work with large-scale industrial data and collaborate with reliability engineers and customers to evolve from predictive analytics to autonomous decision-support.

The role emphasizes end-to-end ML pipelines, deployment in production, and maintaining safety/governance standards across manufacturing operations.

Qualifications

• Bachelor's, Master's or PhD in CS/AI/Robotics/EE/ME or related discipline.
• 4+ years hands-on experience in applied AI/ML.
• Experience with time-series data in industrial contexts.
• Knowledge of ML algorithms for forecasting, anomaly detection, classification and regression.

Responsibilities

• Design, develop, and deploy production-grade AI/ML models for industrial equipment.
• Build end-to-end ML pipelines: data ingestion, feature engineering, training, deployment and monitoring.
• Develop ML solutions for equipment health monitoring, predictive maintenance and RUL estimation.
• Detect anomalies, diagnose faults, and optimize production processes.
• Collaborate with reliability engineers, customer SMEs and software teams; deploy models using MLOps best practices.

Skills

Python programming

Time-series data experience

Strong communication with customers

Education

Bachelor's degree

Master's degree

PhD

Tools

PyTorch

TensorFlow

Scikit-learn

Pandas

NumPy

SciPy

CI/CD

Job description

We are looking for a Senior AI/ML Engineer (4+ years of experience) to build and deploy production-grade AI/ML solutions for industrial equipment, with a focus on motor and pump manufacturing and rotating machinery.

In this role, you will work with large-scale industrial datasets including sensor data, PLC/SCADA systems, historians, maintenance logs, alarms, events, and inspection images/videos to develop intelligent solutions that improve equipment reliability, manufacturing efficiency, and predictive maintenance.

You will collaborate closely with customers, reliability engineers, manufacturing SMEs, and software teams to build AI solutions that evolve from predictive analytics to autonomous decision-support systems capable of forecasting failures, detecting anomalies, optimizing operations, and enabling safe closed-loop actions.

Key Responsibilities

• Design, develop, and deploy production-grade AI/ML models for compressor manufacturing and industrial equipment.


• Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement.


• Develop machine learning solutions for:


• Equipment health monitoring and Predictive maintenance


• Remaining Useful Life (RUL) estimation


• Anomaly detection, Fault diagnosis and root cause analysis


• Process optimization


• Energy efficiency optimization


• Production quality prediction


• Analyze industrial time-series data collected from: Vibration sensors, Pressure sensors, Temperature sensors, Flow sensors, Motor current signals, SCADA/PLC systems, Industrial historians, Maintenance and service records, Alarm and event logs.


• Apply signal processing techniques such as FFT, spectral analysis, wavelet transforms, and statistical feature extraction for rotating equipment diagnostics.


• Develop scalable ML models capable of handling noisy, incomplete, and high-frequency industrial data.


• Collaborate with reliability engineers, maintenance teams, and customer SMEs to understand failure modes and operational constraints.


• Deploy models into production using MLOps best practices, ensuring monitoring, drift detection, automated retraining, and high system reliability.


• Build reusable feature engineering frameworks and model evaluation pipelines.


• Contribute to intelligent decision-support capabilities that enable recommendations for maintenance scheduling, process optimization, and autonomous operational improvements while maintaining safety and governance standards.

Required Qualifications

• Bachelor's, Master's or a PhD degree in Computer Science, AI, Robotics, Electrical Engineering, Mechanical Engineering, or a related discipline.


• 4+ years of hands-on experience in applied AI/ML.


• Strong experience developing machine learning solutions using time-series data.


• Experience working with industrial IoT, manufacturing, process industries, or predictive maintenance applications.


• Solid understanding of signal processing techniques including: FFT, Spectral analysis, Frequency-domain feature extraction, Time-frequency analysis.


• Knowledge of machine learning algorithms for :


• Time-series forecasting, Anomaly detection, Classification, Regression, Clustering.


• Familiarity with optimization or control methods such as Model Predictive Control (MPC) or similar approaches.


• Strong Python programming skills.


• Experience with libraries such as: PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, SciPy.


• Experience deploying ML solutions into production, including: MLOps, Model monitoring, Drift detection, Retraining pipelines, CI/CD.


• Strong software engineering practices including testing, version control, code reviews, and clean architecture.


• Excellent communication skills with experience working directly with customers and cross-functional engineering teams.

Preferred Qualifications

• Experience with compressor systems, rotating equipment, pumps, turbines, motors, or industrial machinery.


• Knowledge of vibration analysis and condition monitoring.


• Experience with Industrial Comm Protocols, SCADA systems, historians, OPC UA, MQTT, or industrial IoT platforms.


• Familiarity with edge AI deployments and real-time inference.


• Experience with reinforcement learning, optimization algorithms, or digital twins.


• Knowledge of self-supervised learning or multimodal AI.

Additional Skills Required

• Experience with cloud platforms such as AWS, Azure, or GCP.


• Exposure to industrial standards related to reliability, safety, or predictive maintenance.

(ref:hirist.tech)