Roles and Responsibilities 

Collaborate with cross-functional teams to understand business requirements and develop AI-based solutions Utilize AI modeling and machine learning techniques to analyze data and create predictive and forecasting models Apply time series models to identify patterns and trends for proactive maintenance strategies Implement regression, logistic regression, decision tree, random forest, xgboost, bagging, and boosting algorithms to optimize predictive models Design and deploy industrial AI and IIOT solutions to enhance operational efficiency and reliability Utilize artificial neural networks, deep learning, computer vision, RNN, CNN, and reinforcement learning to develop advanced AI solutions Collaborate with experts to build recommendation engines and expert systems for AI-based applications Provide technical expertise and support to internal teams and external clients Stay updated with the latest AI technologies and contribute to the development of innovative solutions Communicate complex technical concepts to non-technical stakeholders in a clear and concise manner 

Experience 

5-8 years of experience in the AI & ML industry 

Education Qualification 

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Or Master's degree Statistics, Mathematics, Economics or in a related field 

Certification required 

Relevant certifications in AI, machine learning, or data science are preferred 

Behavioural skills 

Strong analytical and problem-solving skills Excellent communication and presentation abilities Ability to work effectively in a collaborative team environment Adaptability and willingness to learn new technologies Strategic thinking and innovative mindset Strong understanding of Industrial or similar businesses 

Technical Skills 

AI Modeling, ML Modeling, Remaining useful life (RUL), Predictive maintenance, Time series analysis, Regression, Logistic Regression, Decision tree, Random forest, Xgboost, Bagging and boosting algorithms, Industrial AI, IIOT (Industrial Internet of Things), Artificial neural networks, Deep learning, Computer vision, RNN (Recurrent Neural Networks), CNN (Convolutional Neural Networks), Reinforcement learning, Expert systems, Recommendation engine