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Rahul M. has a diverse work experience spanning different industries and roles. In 2014, they worked as an intern at Tata Consultancy Services. In 2015, they joined Mphasis as an Associate Software Engineer, where they worked until 2017. In 2018, Rahul joined Technische Universität Chemnitz as a Master control of signal conditioning box. Rahul later worked as a Research Assistant, focusing on image recreation using sensor fusion and investigating and implementing the K Nearest Neighbor Algorithm and its optimizations. In 2019, while still at Technische Universität Chemnitz, they completed their Master Thesis on the "Random Walk Binary Grey Wolf Optimizer".

In 2020, Rahul joined ROKIN as a Machine Learning Engineer for a few months before transitioning to Yokogawa Insilico Biotechnology GmbH. At Yokogawa Insilico Biotechnology GmbH, they started as a Software Developer, gaining experience in migration of on-site solutions to cloud-based offerings, building containerized applications, and developing automation workflows through CI/CD. Rahul was later promoted to the role of Senior Software Development Engineer, where they continue to contribute to the development and integration of software components for cloud environments.

Rahul M. began their education by completing their ICSE- Tenth(secondary) degree at Baldwin Boys High School from 1997 to 2009. Rahul then went on to study at Mallamma Marimallappa Pre University College from 2009 to 2011, focusing on Physics, Mathematics, Chemistry, and Biology.

Next, Rahul pursued their Bachelor of Engineering (BE) in Electrical and Electronics Engineering from Visvesvaraya Technological University from 2011 to 2015.

After completing their undergraduate degree, they attended Technische Universität Chemnitz from 2017 to 2019, where they obtained a Master's degree in Embedded Systems.

In 2019, Rahul enrolled in Udacity and obtained a Nanodegree in Machine Learning Engineering. Additionally, they acquired several certifications including "Machine Learning Hands on using Python for Data Science" from Udemy, "Hacking Health 2019" from Hacking Health Global, "Optimization Problems and Algorithms" from Udemy, "Artificial Intelligence Foundations: Machine Learning" from LinkedIn, and "Machine Learning and AI Foundations: Classification Modeling" from LinkedIn.