Job Description:
As a Predictive Maintenance Engineer (AI), you will develop and implement AI-driven predictive maintenance models to monitor and optimize equipment health and performance. This role requires a deep understanding of machine learning, data analytics, and industrial systems, allowing you to predict and prevent failures before they occur, thus ensuring operational efficiency and reducing downtime.
Responsibilities
- Design, develop, and deploy predictive maintenance algorithms using machine learning and AI techniques to identify potential equipment failures.
- Collect, clean, and preprocess data from IoT devices, sensors, and industrial equipment to create accurate predictive models.
- Analyze historical data to discover failure patterns, trends, and triggers, improving accuracy and reliability in predictions.
- Work closely with engineering teams to implement predictive maintenance solutions across production and manufacturing environments.
- Continuously monitor and refine predictive models based on real-time data, enhancing their performance and adapting to new insights.
- Collaborate with stakeholders to understand equipment performance requirements and identify opportunities for improving uptime and productivity.
- Document processes and findings, creating reports and presentations to communicate maintenance insights to technical and non-technical teams.
Required Qualifications
- Education: Bachelors degree in Engineering, Data Science, Computer Science, or a related field (Masters or Ph.D. preferred).
- Experience: 3+ years of experience in predictive maintenance, data science, or AI, preferably in industrial or manufacturing settings.
- Technical Skills:
- Proficiency in Python, R, or MATLAB for data analysis and model development.
- Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
- Strong understanding of time-series analysis, anomaly detection, and statistical modeling.
- Experience with industrial IoT platforms, sensor data, and data visualization tools (e.g., Tableau, Power BI).
- Knowledge of cloud platforms (e.g., AWS, Azure) for deploying and scaling predictive models.
- Domain Knowledge: Understanding of industrial equipment, manufacturing processes, and maintenance procedures.
Preferred Skills
- Experience with edge computing and real-time data processing.
- Familiarity with reliability engineering and root cause analysis.
- Knowledge of SCADA systems and industrial automation protocols.
- Strong problem-solving skills and an analytical mindset for addressing complex equipment issues.