Ruby Ineba Briggs

I am an MLOps and Data Engineer with 4+ years building, deploying, and maintaining end-to-end ML systems for high-volume environments. I specialize in designing scalable data and ML pipelines using Airflow, Docker, and cloud platforms to improve operational efficiency.

I have implemented CI/CD, data and model versioning (DVC, MLflow), and reproducible experiments to support model performance monitoring and data accuracy. My work reduced data latency by 30% for a customer churn pipeline and established monitoring frameworks with Grafana.

My technical strengths include Python (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch), advanced SQL, PostgreSQL, MongoDB Atlas, AWS (S3, SageMaker, EC2, Lambda), and orchestration with Apache Airflow. I’ve deployed models to SageMaker, EC2/ECR, Elastic Beanstalk, and Azure using containerization and GitHub Actions.

I bring a strong foundation in deep learning, NLP, and generative AI, and I focus on cross-functional coordination to deliver reliable, versioned feature data and production-ready ML solutions that drive measurable business outcomes.