Freelance Trainer – AI for Predictive Maintenance and Smart Manufacturing
Organisation: AI5 Academy
Job Type: Freelance / Part-Time
Work Mode: Remote / Live Online
Location: India
Schedule: Flexible; weekday or weekend batches
Compensation: Per session or per batch, based on teaching experience and course delivery plan
About the Role
AI5 Academy is looking for a freelance trainer to conduct live online classes for its AI for Predictive Maintenance and Smart Manufacturing course.
The trainer should have practical knowledge of industrial operations, manufacturing data, machine monitoring and machine-learning applications. The role requires clear teaching, live demonstrations, industrial case studies and project-based training.
Key Responsibilities
- Conduct instructor-led online classes for students and working professionals
- Teach predictive maintenance concepts using practical manufacturing examples
- Explain how AI and machine learning are used to identify equipment faults and predict failures
- Cover sensor data, time-series data, anomaly detection and remaining useful life prediction
- Demonstrate industrial data preparation, model training, testing and performance measurement
- Teach condition monitoring for motors, pumps, bearings, turbines and other machinery
- Explain the use of IoT and industrial data in smart manufacturing
- Guide learners through practical assignments, case studies and capstone projects
- Assist in preparing notebooks, datasets, exercises, presentations and project instructions
- Review student projects and provide clear feedback
- Keep course material aligned with current industrial AI practices
- Coordinate with the AI5 Academy academic team regarding batch progress and learner performance
Expected Course Coverage
The trainer should be comfortable teaching most of the following areas:
- Introduction to Industry 4.0 and smart manufacturing
- Predictive, preventive and corrective maintenance
- Industrial sensors and machine-generated data
- Data collection from manufacturing equipment
- Data cleaning and preparation
- Time-series data analysis
- Feature engineering for equipment data
- Anomaly and fault detection
- Classification and regression models
- Remaining Useful Life prediction
- Failure prediction and maintenance scheduling
- Vibration, temperature, pressure and acoustic data analysis
- Machine condition monitoring
- Model evaluation and reducing false alarms
- Industrial IoT and edge AI concepts
- AI-based quality inspection
- Energy and production optimisation
- Digital twins and their manufacturing use cases
- Model deployment and monitoring
- Business ROI of predictive maintenance
- Industrial AI safety, data security and responsible use
Preferred Tools and Technologies
Applicants should have working knowledge of several of the following:
- Python
- Pandas and NumPy
- Matplotlib, Seaborn or Plotly
- Scikit-learn
- TensorFlow or PyTorch
- Jupyter Notebook or Google Colab
- Time-series modelling tools
- Power BI or similar dashboard tools
- IoT sensor platforms
- MQTT or related industrial communication systems
- Cloud platforms such as AWS, Azure or Google Cloud
- GitHub
- Industrial datasets such as NASA turbofan, bearing-failure or machine-sensor datasets
Required Qualifications
- Bachelor’s or Master’s degree in Mechanical Engineering, Electrical Engineering, Electronics, Mechatronics, Industrial Engineering, Computer Science, Data Science or a related area
- Practical experience in predictive maintenance, industrial AI, manufacturing analytics, IoT or machine learning
- Ability to explain technical concepts in simple language
- Experience working with sensor, equipment or time-series datasets
- Good verbal communication and live presentation skills
- Ability to conduct online classes using screen sharing, demonstrations and guided exercises
- Reliable computer, internet connection and suitable online teaching setup
Preferred Candidate Profile
Preference may be given to candidates with:
- Work experience in manufacturing, automotive, energy, oil and gas, utilities, heavy engineering or industrial automation
- Experience building or deploying predictive-maintenance systems
- Prior corporate training, classroom teaching or online teaching experience
- Knowledge of PLC, SCADA, MES, ERP or industrial IoT systems
- Published projects, GitHub work, case studies or research related to industrial AI
- Experience working with maintenance, reliability or plant operations teams
Practical Project Expectations
The trainer should be able to guide learners through projects such as:
- Machine-failure prediction from sensor data
- Bearing-fault or motor-fault detection
- Remaining Useful Life prediction
- Equipment anomaly-detection system
- Predictive-maintenance monitoring dashboard
- Maintenance-priority recommendation system
- AI-based manufacturing quality inspection
- Smart-factory capstone using machine and production data
Trainer Deliverables
- Live instructor-led sessions
- Session-wise lesson plan
- Presentations and demonstration files
- Practice datasets and assignments
- Guided projects
- Assessment questions
- Capstone project support
- Student evaluation and batch feedback
How to Apply
Please submit:
- Updated CV
- Short trainer profile
- Areas you can teach from the listed course coverage
- Details of industrial or predictive-maintenance projects completed
- Prior training or teaching experience, if any
- GitHub, LinkedIn, portfolio or research links
- Current location
- Available days and time slots
Pay: ₹9,374.19 - ₹30,000.00 per month
Benefits:
Work Location: Remote
