AI/ML | Machine Learning | Deep learning
Send a job offer directly to this candidate
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)
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)
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.
