Parthipan M

My name is Parthipan, a passionate engineer with a strong foundation in mechanical systems and an evolving interest in data-driven technologies. I hold a B.E. in Mechanical Engineering (2021) with a 76% aggregate and am a certified ASME GDTP Professional.

I am currently transitioning into the field of Artificial Intelligence and Machine Learning, driven by a deep interest in solving real-world engineering problems through data science. I believe that ML can transform industries by enabling smarter quality control, predictive maintenance, and process automation.

To support this transition, I am pursuing a PG Program in AI/ML from Great Learning, in collaboration with the University of Texas at Austin. Through this program, I have worked on hands-on projects in:

Regression, classification, and ensemble methods

Deep learning and sentiment analysis

Predictive modeling in real-world scenarios
I aim to apply these skills in engineering and manufacturing domains to drive innovation and efficiency.

Dimensional Engineer – Ford (via SACH Engineering)
Duration: 2 Years

Worked on dimensional validation and tolerance stack-up analysis using the Six Sigma methodology

Analyzed over 5,000 part samples using GD&T-based virtual assembly in industry-grade tools

Ensured part quality, process capability, and compliance with design standards

Dimensional Engineer – Stellantis (via Capgemini)
Current

Continuing in a similar role focused on dimensional quality assurance and product development

Collaborating with cross-functional teams to support production readiness through data analysis and virtual build simulations

Currently pursuing an AI/ML program from Great Learning in collaboration with the University of Texas at Austin, which delivers high-quality education with a strong focus on both theoretical concepts and practical application.

As part of the curriculum, we have built at least three hands-on projects for each major topic, including:

Machine Learning: Regression, classification, and ensemble methods (like Random Forest, XGBoost)

Deep Learning: Artificial Neural Networks (ANN) applied to tasks such as sentiment analysis and image/text classification

This structured, project-based learning has strengthened my ability to apply ML and DL techniques to real-world business and engineering problems. The program also emphasizes model evaluation, cross-validation, and feature engineering, providing end-to-end exposure from data preprocessing to deployment.