Open to opportunities

Rayan Ali

@rayanali

Message

I am an AI/ML engineer delivering production-ready ML solutions.

United States

Message

What I'm looking for

I seek a leadership role building production-grade ML systems with strong MLOps, explainability, and cross-functional collaboration in regulated domains (healthcare/finance), where I can mentor teams and drive measurable business impact.

I am an AI/ML engineer and data scientist with over nine years building scalable machine learning solutions across fintech, healthcare, and logistics. I design predictive models and deep learning architectures, lead MLOps implementation with AWS SageMaker, MLflow and Airflow, and translate data into measurable business outcomes—reducing inventory stockouts 22% and improving fraud capture by 45%.

I have led teams of 6+ engineers and scientists, delivered clinical-grade imaging models (92% sensitivity) and HIPAA-compliant inference pipelines, and guided FDA-aligned validation and regulatory submissions. I focus on reproducible experimentation, low-latency model serving, explainability (SHAP/LIME), and mentoring cross-functional teams to operationalize impactful AI systems.

Experience

Work history, roles, and key accomplishments

Current

Lead AI/ML Architect

Current

ForecastFlow

Mar 2022 - Present (4 years 3 months)

Architected and led deployment of cloud-native ML forecasting systems for enterprise supply chain and retail use cases, reducing inventory stockouts by 22%. Established MLOps pipelines with AWS SageMaker, MLflow and Airflow and directed a team of 6+ engineers to implement CI/CD and low-latency FastAPI model serving.

AWS Sagemaker MLFlow Airflow Docker fastAPI MLOps

Senior Data Scientist

InDataLabs

Nov 2018 - Dec 2021 (3 years 1 month)

Led development of deep learning pipelines for disease detection from OCT images, achieving 92% sensitivity in clinical evaluation and optimizing edge deployment with ONNX and TensorRT for real-time inference. Built semi-supervised labeling tools, validated models with ROC-AUC and Cohen's Kappa, and supported FDA submission and clinical validation workflows.

CNN Onnx TensorRT Medical Imaging ROC AUC EHR Integration

Machine Learning Engineer

Markovate

Jan 2016 - Oct 2018 (2 years 9 months)

Engineered fraud detection and credit scoring models using XGBoost and logistic regression, increasing precision by 17% and integrating low-latency risk pipelines with transactional APIs. Designed Airflow-based data pipelines, implemented SHAP/LIME explainability and conducted A/B and uplift testing to validate ML impact on approval rates.

XGBoost Logistic Regression Airflow SQL Python SHAP LIME RESTful APIs