Professional Overview
Junior MLOps Engineer who productionized real-time solar forecasting microservices on GCP using Docker and CI/CD. Orchestrated automated monthly challenger vs champion retraining, enforced by PSI/KS drift monitoring and observability logging. Achieved 45% MAE reduction for a live MILP-based EMS.
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.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
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.
