Professional Overview
Machine Learning and AI Engineer with hands-on experience developing 10+ deep learning and ML workflow projects using Python, PyTorch, and TensorFlow. Skilled in building ANN, CNN, RNN, and NLP models with performance accuracies reaching 95%+ on benchmark datasets. Experienced in designing end-to-end ML workflows , data preprocessing and model eval
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Developed and optimized AI/ML models using Python, PyTorch, and TensorFlow.
- Built scalable ML workflows for data preprocessing, training, and evaluation.
- Improved model accuracy and reduced training time through optimization technique
- Engineered data pipelines for structured and unstructured datasets.
- Implemented deep learning models including ANN, CNN, and RNN architectures.
Key Achievements / KPIs
- Built AI/ML models with 95%+ accuracy using PyTorch and TensorFlow.
- Improved ML workflow efficiency by 40% through automation.
- Developed ANN, CNN, and RNN models with 30% lower loss.
- Increased data processing performance by 35%.
Education
Field of Study: Mining Engineering
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
