Artificial Intelligence Engineer - Cognitive Maintenance

Job Description:

As an innovative Artificial Intelligence (AI) Engineer - Cognitive Maintenance, you will work by combining advanced data science and artificial intelligence know-how with industrial operations systems. Your main goal is to develop algorithms and intelligent models to forecast equipment failures before they occur, optimize asset reliability, prescribe corrective actions, and ultimately reduce unplanned downtime.

You’ll face complex challenges involving large-scale time-series data, IOT sensor data processing, and machine learning applications; all these are your tools to ensure our cognitive maintenance solutions remain the gold standard reference for the global industrial AI industry.

The ideal candidate is passionate and has a professional track record of combining artificial intelligence with physical data (vibration, temperature, sound, pressure, etc), thrives in a fast-paced environment, and is eager to make an impact on the industrial world.

Job Responsibilities:

1. Predictive Maintenance AI Model Development:
   ○ Design and implement machine learning and deep learning models for our Predictive Maintenance applications.
   ○ Optimize models for performance, scalability, and accuracy.
   
2. Data Processing and Analysis:
   ○ Analyze, understand, and pre-process sensor data streams to identify patterns that predict equipment and industrial asset failures.
   ○ Perform exploratory data analysis to identify patterns and insights.
   
3. Deployment and Maintenance:
   ○ Deploy AI models to production environments using best practices. ○ Monitor, maintain, and update deployed models to ensure ongoing relevance and performance. ○ Build scalable, real-time models for low-latency predictions.
   
4. Collaboration and Problem Solving:
   ○ Collaborate with engineers to improve data pipelines and enhance model accuracy. ○ Identify AI opportunities within existing workflows and propose innovative solutions.
   
5. Research and Innovation:
   ○ Stay updated with the latest advancements in AI technologies and methodologies.
   ○ Research and stay up to date with academic literature and state-of-the-artcondition monitoring techniques, translating these ideas and innovations into workable and deployable solutions.
   ○ Work with the engineering team to create experiments and failure datasets; you will use real data from machines to validate your hypotheses, develop new models, and improve current models.

6. Performance Monitoring:
   ○ Develop tools and frameworks to monitor model behavior in real-time and enable data-driven maintenance decisions.
   ○ Troubleshoot issues and ensure model reliability under varying conditions. ○ Continuously improve and validate models based on real-world performance, test results, and feedback.
   
   7. Documentation and Reporting:
   ○ Document AI models, processes, and systems for internal use.
   ○ Create reports on AI performance metrics and insights for stakeholders.
   
   Qualifications:

   Education: Bachelor’s or Master’s degree in Computer Science, Business Analytics, Artificial Intelligence, Data Science, or a related field.

   2. Experience:
   ○ At least 1 year of experience in developing and deploying analytics models. ○ Proven experience and expertise in machine learning, time-series analysis, andanomaly/failure detection.

   3. Technical Skills:
   ○ Proficiency in programming languages such as Python, R, or Java. ○ Experience with machine learning frameworks and libraries (TensorFlow, PyTorch, Scikit-learn). ○ Familiarity with cloud platforms (AWS, GCP, Azure) for AI deployment. ○ Strong understanding of signal processing concepts and hands-on experience with industrial sensor data (e.g., vibration, current, temperature, pressure). ○ Ability to read, interpret, and apply insights from academic literature andstate-of-the-art research in condition monitoring and fault diagnosis. ○ Experience designing experiments to validate hypotheses and benchmark models.

   4. Soft Skills:
   ○ Strong analytical and problem-solving skills. ○ Effective communication and collaboration abilities to work across teams. ○ A curious mindset and a drive to innovate and experiment. ○ Strong problem-solving skills and ability to handle noisy, high-dimensional data.

   5. Preferred:
   ○ Prior experience working in industrial or manufacturing environments.
   ○ Familiarity with both academic research and real-world applications in condition monitoring, fault diagnosis, and prognostics (e.g., vibration-based methods,model-based vs. data-driven approaches).
   ○ Experience translating academic methods into robust, production-ready algorithms.