Professional Overview
Results-driven MLOps Engineer and Data Scientist with experience in designing, deploying, and optimizing end-to-end machine learning pipelines and AI-driven solutions using Python. Hands-on expertise in CI/CD for ML, workflow automation, experiment tracking, model monitoring, and performance improving in production environments.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Validated ML models using cross-validation and performance metrics.
- Tuned machine learning models to reduce overfitting and improve accuracy.
- Documented experiments, workflows, and model performance metrics.
- Assessed model performance using Accuracy, F1-score, Recall, and RMSE.
- Implemented Logistic Regression, Random Forest, KNN, and Decision Trees.
Key Achievements / KPIs
- Improved data quality by 30% through data cleaning and preprocessing.
- Increased model accuracy by 15% using feature engineering techniques.
- Developed ML models achieving up to 72% accuracy on test datasets.
- Built predictive models with 75–80% accuracy using Random Forest and KNN.
Education
Field of Study: MBA with specialization in Business Analytics
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
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.
