Professional Overview
Results-driven AI/ML & MLOps Engineer with strong experience designing, deploying, and monitoringscalable end-to-end machine learning systems. Proven ability to transition models from research toproduction using CI/CD pipelines, containerization, and cloud-native AWS services. Skilled in modelexperimentation, reliability engineering, data drift detection, and explainable AI, with a strong focus onautomation, reproducibility, and business-aligned outcomes.
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.
