Mohammed Badar Uz Zama

About Mohammed Badar Uz Zama

I’m an AI/ML Engineer with 3+ years of experience building and deploying production-grade machine learning systems that deliver measurable business impact across healthcare, supply chain, and enterprise analytics.My work spans the full ML lifecycle—from problem formulation and data engineering to model development, deployment, and monitoring in real-world environments. I’ve led initiatives that improved demand forecasting accuracy by 18%, reduced manual contract review by 35% using NLP automation, and increased recommendation engagement by 22% through scalable recommender systems.Technically, I specialize in supervised and deep learning (XGBoost, LightGBM, LSTM, CNNs, Transformers), NLP (BERT, NER, document intelligence), time-series forecasting, and recommender systems. I’m equally strong in MLOps, with hands-on experience building scalable pipelines using Python, SQL, Dask, MLflow, and deploying containerized models on AWS and GCP using Docker, Kubernetes, FastAPI, and CI/CD workflows.I enjoy working closely with cross-functional teams to translate complex business problems into deployable AI solutions that scale, perform, and create real value. I’m especially interested in roles where AI is used to drive decision intelligence, automation, and data-driven product innovation. Open to AI/ML Engineer, Machine Learning Engineer, and Applied Scientist opportunities (US & Remote).

Experience

1. Ai Ml Engineer

   Cardinal Health

   Jun 2024 — Present · US

   Designed and deployed end-to-end demand forecasting models using XGBoost and LSTM architectures in Python (NumPy, Pandas), improving forecast accuracy by 18% across high-volume SKUs.• Built an NLP-based contract intelligence solution leveraging BERT and Transformer models for document classification and entity extraction (NER), reducing manual review effort by 35%.• Developed scalable ML pipelines using Dask and MLflow containerized models with Docker and deployed via Kubernetes on AWS SageMaker for real-time inference.• Implemented model monitoring framework using Prometheus and Weights & Biases to track drift, latency, and performance degradation, improving model reliability in production environments.• Architected recommender system for product substitution and cross-sell optimization using collaborative filtering and deep learning models, increasing recommendation engagement by 22%.• Partnered with supply chain and analytics stakeholders to translate business requirements into deployable ML solutions, integrating APIs via FastAPI and REST services into enterprise applications.

Education

• Birla Institute of Technology and Science, Pilani

  Bachelor of Technology - BTech, Nil

  2018 — 2022

• DePaul University

  Master of Science - MS, Data Science