Open to opportunities

jash dhamecha

@jashdhamecha

Message

Early-career machine learning engineer skilled in model training, tuning, and production-driven experiments.

India

Message

What I'm looking for

I seek remote-friendly roles where I can apply model training and tuning skills, work asynchronously with distributed teams, and deliver measurable production impact.

I am a remote-ready, early-career Machine Learning Engineer focused on training, tuning, and evaluating machine learning and deep learning models to drive production improvements. I collaborate effectively with distributed teams and deliver experiment-driven results for production-oriented ML systems.

In recent roles I applied data-driven analysis to optimize workflows (improving efficiency by ~15%), trained models for customer behavior prediction (improving forecasting accuracy by ~20%), and built TensorFlow-based recommendation models (increasing engagement by ~30%). I also reduced fraud-detection false positives by ~25% through systematic model experimentation and tuning.

My background includes a B.Sc. in Information Technology (AI) and hands-on projects such as an automated trading bot, a disaster risk management system using geospatial data, and optimizing deep learning workflows with NVIDIA AI tools. I prioritize reproducible workflows, asynchronous collaboration, and measurable model impact.

Experience

Work history, roles, and key accomplishments

ML Engineer

FusionTech International

Jul 2025 - Dec 2025 (5 months)

Applied data-driven analysis to optimize workflows, improving efficiency by ~15% and led digitization and reporting initiatives to enable remote visibility into operations.

Machine Learning Data Analysis TensorFlow Model Evaluation Experimentation Reporting Remote Collaboration Workflow Optimization

Applied Machine Learning Intern

Prudent Technology

Nov 2024 - Apr 2025 (5 months)

Trained and optimized ML models for customer behavior prediction, improving forecasting accuracy by ~20% and built TensorFlow recommendation models that increased engagement by ~30%; reduced fraud false positives by ~25%.