Subhabrata Banerjee

Subhabrata Banerjee has extensive work experience in the field of software engineering and machine learning. Subhabrata is currently serving as the Lead Machine Learning Engineer at Imperva since 2022. Prior to that, they worked as a Staff Software Engineer at Juniper Networks from 2019 to 2022, where they were responsible for developing infrastructure data analysis and recommendation systems, implementing various network security ML use cases, and managing the end-to-end ML pipeline. Before that, they held the position of Member Of Technical Staff 4 at Nutanix from 2018 to 2019.

Subhabrata also worked at Oracle as a Principal Member Of Technical Staff from 2017 to 2018, where they focused on implementing anomaly detection for threats using unsupervised clustering ML algorithms. Subhabrata also contributed to the development of the Oracle Cloud Security Product.

Prior to that, they served as a Software Engineer III at Cisco from 2015 to 2017, working on automation frameworks for Cisco security products and conducting technical training sessions. Subhabrata started their career as a Senior Software Development Engineer in Test at McAfee in 2014, where they worked on messaging security products and conducted proof of concept presentations for automation possibilities.

Subhabrata has also worked at Symantec as a Senior Software Engineer, Tata Consultancy Services as an Assistant System Engineer, Lore Software Solutions Private Limited as a Software Engineer, and Honeywell as a Project Trainee.

Subhabrata Banerjee completed a Bachelor of Engineering (B.E) degree in Information Technology from West Bengal University of Technology, Kolkata, from 2004 to 2008. In terms of additional certifications, Subhabrata Banerjee has obtained various certifications from LinkedIn, including Machine Learning and AI Foundations, Introduction to Prompt Engineering for Generative AI, Introduction to Large Language Models, Transformers: Text Classification for NLP Using BERT, Machine Learning Foundations: Probability, AWS Essential Training for Architects, Amazon EC2 Load Balancers, Statistics Foundations: 2, Statistics Foundations: Probability, Amazon Web Services: Data Services, Architecting Big Data Applications: Batch Mode Application Engineering, Cassandra Data Modeling Essential Training, Learning AWS CloudFormation, Building and Deploying Deep Learning Applications with TensorFlow, Deploying Scalable Machine Learning for Data Science, NLP with Python for Machine Learning Essential Training, Building a Recommendation System with Python Machine Learning & AI, and Elasticsearch Essential Training.