Benjamin Sprayberry

I’m a Senior AI Engineer focused on building cloud-native AI platforms that scale in production. I’ve architected large-scale inference systems on AWS, delivering measurable performance gains like reducing latency by 40% across high-volume workloads.

I design distributed microservices and streaming pipelines using Python and Kubernetes to support real-time, high-throughput applications. At Amazon, I’ve implemented fault-tolerant architectures and observability with Prometheus and Grafana, reducing downtime by 22% and improving throughput by 18% while keeping service reliability strong under peak traffic.

I also integrate LLMs and ML models into enterprise APIs—improving workflow automation accuracy by 30% and boosting internal document processing efficiency by 35% using OpenAI APIs, vector databases, and RAG-style pipelines.

Previously, I delivered AI/ML products in real-world environments: building computer vision models with PyTorch and OpenCV (27% detection accuracy lift), deploying RESTful FastAPI services for over 2M monthly requests, and strengthening data processing with Spark and Airflow (40% throughput). I bring this full-stack perspective—backend and front-end where needed—along with MLOps and DevOps discipline (Terraform, GitHub Actions, Kubernetes) to ship faster, deploy more consistently, and mentor engineers through architecture reviews.