Your Role and Responsibilities

GTS Analytics team in Kyndryl is building new innovative AIOPS solution by combining big data with Machine Learning and Deep Learning

AIOps refers to multi-layered technology platforms that automate and enhance IT operations by using analytics and machine learning to analyse big data collected from various IT operations tools and devices, in order to automatically spot and react to issues in real time. AIOps bridges three different IT disciplines—service management, performance management, and automation—to accomplish its goals of continuous insights and improvements.

Some of the Solutions we work involve the following

• Proficiency in at least one statistical tool language (R, Python) with a strong preference towards Python
• Deep expertise in statistical, machine learning and deep learning techniques including but not limited to classification, Regression, Anomaly Detection, Multivariate Correlation, Forecasting, Optimization, Topic Modelling, Clustering, False Positive/False Negative Reduction, Imbalance Class Problems, Novelty Detection, Casual Inferences, Statistical Tests, Evaluation Methodologies, etc
• Thorough understanding and experience in ML algorithms including but not limited to Decision Tree, Random Forest, Naive Bayes, Support Vector Machines, XGBoost, Logistic Regression, etc
• Thorough understanding and experience in Neural Network, Deep Learning (CNN, RNN, LSTM, Auto encoder, HTM, etc)
• Experience with Structured, Semi-structured and high dimensional data for data mining and information retrieval purposes
• Strong knowledge and Capability to develop production ready solution using Python
• Strong communication written and oral, structured problem solving and story-boarding skills
• Ability to translate broad business strategies into clear, specific analytics led use-cases and design business deliverables and solutions.
• Real time anomaly detection solutions that proactively identify service impacting incidents and prevent system downtimes. This is done by leveraging an ensemble of Deep learning and LSTM / HTM models.
• Natural Language Processing for entity extraction, classification, topic clusters and relationship extraction
• Text Analytics in human generated service management tickets and correlation with ITIL service management event tickets for event noise reduction. Apply Natural Language Classification and RNN algorithms to automatically route tickets, bias detection, ticket de-duplication, cognitive ticket analysis and actionable insights etc
• Log Analysis – Text mining, message clustering / templatization, Logs to metrics, anomaly detection, event annotation and sequencing.
• Learn Log Message Sequence for each mainframe batch job and Identify Anomalies during job runs using sequence mining techniques and provide early warning / alerts.
• Cloud Migration – Patterns-based discovery optimization: Identify potential business application boundaries using algorithmic approach from Cloudscape data.