Senior Engineer or Lead -AI/ML

WonderBiz Technologies Pvt.; Thane; On-site; INR 1,600,000 - 2,400,000; Full time

Job summary

WonderBiz Technologies Pvt. is seeking a Senior AI/ML Engineer to lead development of production-grade models for industrial equipment, focusing on predictive maintenance and health monitoring.

You will handle time-series data from sensors, PLC/SCADA systems, and maintenance logs, driving end-to-end ML pipelines and real‑world deployments. You will work with reliability engineers and software teams to evolve analytics into autonomous decision-support, forecasting failures, detecting anomalies,

Qualifications

• 4+ years hands-on AI/ML experience with time-series data.
• Experience in industrial IoT, manufacturing, or predictive maintenance.
• Strong Python and ML framework skills; able to deploy models to production.
• Ability to collaborate with customers and cross-functional teams.

Responsibilities

• Design, develop, and deploy production‑grade AI/ML models for industrial equipment.
• Build end‑to‑end ML pipelines: ingestion, feature engineering, training, deployment, monitoring.
• Develop solutions for health monitoring, PM, RUL, anomaly detection, and process optimization.
• Collaborate with reliability engineers, SMEs, and software teams.
• Deploy models with MLOps, monitoring, drift detection, and retraining pipelines.
• Create reusable feature engineering frameworks and evaluation pipelines.

Skills

Time-series analysis

Python

Machine learning

Deep learning

ML model deployment

MLOps

Signal processing

PyTorch

TensorFlow

Scikit-learn

CI/CD

Education

Bachelor's/Master's/PhD in CS/AI/EE/ME

Tools

PyTorch

TensorFlow

Scikit-learn

Pandas

NumPy

SciPy

Job description

Job Opportunity - AI ML Lead/Senior Engineer

Role: AI ML Lead/Sr. Engineer

Experience: Minimum 4+ years

Location: Thane

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 Insudtrial 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 Skill Required

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

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