Open to opportunities

Mubeen Butt

@mubeenbutt2

Message

AI/ML engineer and data scientist delivering production-ready deep learning and MLOps solutions.

United States

Message

What I'm looking for

I seek roles building end-to-end ML systems with strong MLOps practices, collaborative teams, regulatory-compliant projects, and opportunities to lead and scale high-impact AI products.

I am an AI/ML engineer and data scientist with over 9 years of experience building scalable machine learning solutions across fintech, healthcare, and logistics.

I design predictive models and deep learning architectures and deploy production-ready systems using Python, TensorFlow, PyTorch, XGBoost, and AWS SageMaker, focusing on measurable business impact.

I have led teams, standardized MLOps pipelines with MLflow, Airflow, Docker, and implemented CI/CD, model monitoring, and explainability (SHAP, LIME) to meet regulatory and operational requirements.

Key achievements include reducing inventory stockouts by 22% with forecasting systems, achieving 92% sensitivity on diabetic retinopathy models, and improving fraud capture by 45% through real-time detection pipelines.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer & Data Scientist

Current

Forecast Flow

Apr 2022 - Present (3 years 8 months)

Architected and led deployment of cloud-native ML forecasting systems for enterprise supply chain and retail, reducing inventory stockouts by 22% and standardizing reproducible MLOps pipelines.

AWS Sagemaker MLFlow Airflow Docker fastAPI Model Monitoring MLOps Team Leadership

Senior Data Scientist

Clarity

Jan 2019 - Feb 2022 (3 years 1 month)

Led development of deep learning pipelines for OCT medical image disease detection achieving 92% sensitivity and optimized edge deployments with ONNX/TensorRT for real-time clinical inference.

Onnx TensorRT Medical Imaging Active Learning Regulatory Compliance

Machine Learning Engineer

mpsWORKS

May 2016 - Dec 2018 (2 years 7 months)

Engineered fraud detection and credit scoring models using XGBoost and logistic regression, increasing precision by 17% and deploying low-latency risk pipelines integrated with transactional APIs.