Build ML Anomaly Detection Backend

Cloud Computing, Machine Learning (ML), Statistical Analysis, Spark, Kubernetes, Anomaly Detection, API Development, Microservices, CI/CD, FastAPI · Sentinel Ledger: Real-Time Risk & Anomaly Intelligence Backend

THE ROLE

We're hiring a senior backend architect (or a small team, if bidding as an agency) to build a production-grade, real-time transaction-monitoring system from the ground up. This isn't a prototype or a portfolio piece — the end result needs to run live, under real load, in a real enterprise environment, with real money moving through it.

Transactions flow in continuously, get scored for risk in near-real-time by trained ML models, and surface actionable alerts through a fast, well-documented API — all while the whole stack stays observable, scalable, and secure.

We won't accept stubbed-out logic, hardcoded thresholds standing in for real models, mocked data streams, or anything that only works in a demo. If it can't survive production traffic, it doesn't count.

WHAT YOU'LL BE BUILDING

1. Ingestion Layer - A distributed streaming pipeline (Kafka + Spark Streaming) that keeps pace with high transaction volume in real time, with no backlog buildup - Validation, cleanup, and schema enforcement built directly into the pipeline, not bolted on after

2. Feature Store - A single, centralized feature repository - St