Real-Time ML Platform Engineer for Risk & Fraud

Quiet Capital; United States; On-site; USD 166,600 - 208,300; Full time

Job summary

Quiet Capital is looking for an experienced machine learning engineer to join our Machine Learning Platform team. You will build and maintain real-time inference services that score models for our risk decision engine with a focus on low latency and high availability.

Ideal candidates will have over 5 years of experience in machine learning engineering, strong backend engineering skills in Python, and the ability to handle model deployment infrastructure effectively. Competitive salary range is offered.

Qualifications

• 5+ years in machine learning engineering, backend software engineering, MLOps, or a closely related field.
• Production ML service experience — deploying, serving, and operating models in low-latency, high-availability contexts.
• Strong backend engineering fundamentals in Python.

Responsibilities

• Build and operate the real-time inference service for the risk decision engine.
• Own model deployment infrastructure including CI/CD.
• Build model observability: availability, latency, and error monitoring.

Skills

Machine learning engineering

Backend software engineering

MLOps

Python

SQL

Tools

FastAPI

Flask

Redis

DynamoDB

Kafka

Job description

Quiet Capital is looking for an experienced machine learning engineer to join our Machine Learning Platform team. You will build and maintain real-time inference services that score models for our risk decision engine with a focus on low latency and high availability.

Ideal candidates will have over 5 years of experience in machine learning engineering, strong backend engineering skills in Python, and the ability to handle model deployment infrastructure effectively. Competitive salary range is offered.