The posting
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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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. Experience: ○ At least 3 years of experience in developing and deploying analytics models. ○ Proven experience and expertise in machine learning, time-series analysis, andanomaly/failure detection. 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. 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. 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. WHAT WE OFFER 18-month hyper-growth trajectory for your career. We eliminate corporate bureaucracy and tenure-based politics—your velocity is determined purely by your impact. High performers can fast-track into clear leadership tracks within 18 months: -> The Technical Track (Senior AI Engineer / Tech Lead): Command deeper architectural ownership, pioneer core AI strategies, and drive high-stakes technical decisions, or ->The Leadership Track (AI Engineering Lead / Team Lead): Scale your impact through people management, project orchestration, and elite team-building. We provide the launchpad; your ambition defines the altitude. If you are looking for an artificial ceiling on what you can achieve, you won't find one here. Zero bureaucracy, direct executive alignment. Bypass middle management and work face-to-face with the founders, CTO, and AI leaders to architect solutions and steer the company’s technological future. Uncapped technical variety. Accelerate your expertise by architecting solutions across a highly diverse international portfolio. You will regularly navigate complex, real-world datasets and distinct regulatory environments spanning APAC, Korea, Japan, and Europe. Complete lifecycle ownership from Day 1. Ship and scale production-grade AI systems that solve messy, real-world industrial problems. You will drive the entire pipeline, from raw model development and rigorous validation to live deployment and autonomous continuous improvement. Exponential career velocity. Fast-track your path to technical leadership. You will build elite-level mastery across AI/ML, complex time-series data, and MLOps, while scaling your impact through direct team mentorship and high-stakes architectural ownership. Architect the future of autonomous industry. Take ownership of high-stakes AI problems, developing advanced agentic systems, root cause analysis models, and prescriptive AI frameworks. You won't just deploy existing models—you will pioneer our next-generation Cognitive Maintenance technology at the intersection of embedded systems and industrial AI. Zero sandbox fluff—your code dictates real-world operations. Your models will run live on massive industrial assets, where your engineering decisions directly determine systemic reliability, multi-million-dollar productivity metrics, and high-stakes operational outcomes.
