Job Description & Details
This is a solid core MLOps role at Early Warning, the company behind heavy hitters like Zelle and Paze. You will be bridging the gap between experimental data science and high-stakes real-time fraud detection systems, making sure predictive models don't just work in a Jupyter notebook, but survive the brutal realities of production banking infrastructure.
What You'll Actually Be Doing
You'll spend your days building and stabilizing pipelines that take machine learning models from raw code to real-time serving environments. Expect to spend a lot of time wrestling with orchestration processes, optimizing AWS infrastructure, and setting up robust observability tools to catch model drift before it impacts millions of users. You'll act as the connective tissue between data scientists who want to ship fast and platform engineers who need to keep the banking system secure and compliant.
The Core Tech Stack
You need to be fluent in Python and comfortable operating inside AWS ecosystems. Containerization is non-negotiable here, so deep hands-on experience with Docker, Kubernetes, and modern CI/CD pipelines will make or break your day-to-day. On top of that, you'll need working familiarity with MLOps orchestration frameworks like MLflow or Kubeflow to manage model versions and artifact registries effectively.
Interview Expectations
Expect the hiring team to test your real-world battle scars by asking how you handle debugging a model that is silently failing or drifting in production without throwing explicit errors. They want to see how you think about monitoring systems, feature stores, and latency bottlenecks in real-time inference setups. Another favorite line of questioning will revolve around your containerization strategies and how you safely roll back broken model deployments in a high-availability fintech environment.
Application Advice
Skip the generic fluff and tailor your resume to heavily feature your hands-on work with AWS, Kubernetes, and production-grade MLOps tools like MLflow. If you've previously built monitoring systems to catch data drift or optimized real-time model inference latencies, put those metrics right at the top of your experience bullets to clear the ATS and catch the engineering manager's eye.
