Engineering reliable platforms. Building what's next.

Problem

Streams of security log events are too noisy for fixed rules alone, but sending everything to an analyst does not scale.

Approach

A LangGraph supervisor lets an LLM decide which tools to call for each event — heuristic scoring, a SQL lookup of recent logins, IP reputation, web search — and produce a plain-English verdict, alerting Slack when risk is high.

Problem

Queue-driven workloads often sit idle or fall behind because CPU and memory are poor signals for how much work is waiting.

Approach

Producer and consumer services for AWS SQS and Kafka, plus a CPU-intensive workload, deployed with KEDA ScaledObjects that scale on queue length, consumer lag or CPU load.

Problem

Training, comparing and deploying models by hand is slow and hard to reproduce. Teams need a repeatable path from training run to a scalable inference endpoint.

Approach

A Kubeflow pipeline trains several models in parallel with different learning rates, selects the most accurate one and deploys it automatically as an auto-scaling KServe inference service, with artifacts stored in MinIO.