Nithin Karkal

Delivered FEA calculation support to 2 early-stage startups, by running solar-panel fixation strength and CFD analysis and structural-strength FEA for a medical-device startup's wearable device designs, resulting in validated, manufacturing-ready designs. Began prerequisite preparation for a structured transition into AI/ML, by starting Data Scientist coursework at Codebasics covering Python, DSA, model development and ML engineering foundational base to build production-grade ML/MLOps work.

- Led a 15-engineer global CAE team; built GKN's first offshore R&D center in India (-9% project cost) - Drove global rollout of standardized simulation methods, cutting physical testing costs by 15%; co--founded the Global Expert Network - Delivered validated FEA for hybrid transmissions, EVs & driveline components - Applied MSC Adams multi-body simulation; engineered a CAE-driven Digital Twin for a 3-in-1 hybrid transmission

FEM analysis of Locomotive Structures & Engines: Validated structural strength and fatigue life for locomotive cabins and axle structures through static, dynamic, and nonlinear FEA (HyperMesh, OptiStruct), clearing designs for further development. Also performed topology optimization, bolt/weld strength calculations, and rivet-path checks to produce optimized, verified sheet-metal fixture interfaces that met strength requirements while supporting weight and material efficiency goals.

Defence Research & Development Organization (DRDO) — Bangalore, India 09/2004 – 08/2006 • Designed and analyzed aerostructures using SolidWorks, CATIA V5, and Nastran; ensured structural integrity under extreme high-G load tests

Diploma in Machine Learning Engineering and MLOps, delivered by LIORA in academic partnership with Pantheon-Sorbonne University, Paris — progressing directly from LIORA's Data Scientist Diploma. Advanced coursework in containerized deployment( with Kubernets & Cloud ), workflow orchestration (Airflow), experiment tracking (MLflow, Weights & Biases), model serving (BentoML). Culminated in a ~170-hour capstone project applying CNNs, LLMs, RAG & agentic workflows to real, uncleaned data.

Full-lifecycle Data Science diploma from LIORA, awarded in academic partnership with Pantheon-Sorbonne University, Paris (issued June 2026). Coursework spanning statistics, exploratory data analysis, classical machine learning, and deep learning fundamentals — scikit-learn, TensorFlow/PyTorch, computer vision, SQL — covering the full lifecycle from data wrangling to model evaluation.