Remote Engineering Lead, Credit Risk & MLOps

Openkrill; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Openkrill seeks an hands-on Engineering Lead to guide the Credit Risk Engineering team, designing and operating backend services and risk APIs that integrate with Kiwi's lending products. You will mentor engineers, review code, and contribute directly to critical work, collaborating with Credit Risk and Data Science to deploy validated models in production.

You will help improve reliability, latency, and observability across services, while advancing MLOps and deployment automation in a

Qualifications

• Experience leading engineers while contributing hands-on in design, implementation, and production support.
• Strong backend engineering with TypeScript and Node.js; building and operating APIs and microservices.
• Experience deploying and operating containerized services on AWS using Docker and CI/CD.
• Experience in production ML with MLOps: deployment, versioning, monitoring, rollback.

Responsibilities

• Lead the Credit Risk Engineering team, setting technical direction and mentoring engineers.
• Design, build, and operate backend services and APIs that integrate risk capabilities.
• Improve reliability of credit decision flows across services and dependencies; manage latency, timeouts, retries, and observability.
• Collaborate with Credit Risk and Data Science to bring models into production and improve MLOps practices.
• Improve architecture and maintainability of risk services and integrations while addressing technical debt.

Skills

Team leadership

System design

Backend engineering

MLOps

Python

Docker

CI/CD

SQL & PostgreSQL

Tools

TypeScript

Node.js

PostgreSQL

Docker

AWS

GitHub Actions

FastAPI

LightGBM

scikit-learn

Snowflake

Airflow

dbt

Job description

Openkrill seeks an hands-on Engineering Lead to guide the Credit Risk Engineering team, designing and operating backend services and risk APIs that integrate with Kiwi's lending products. You will mentor engineers, review code, and contribute directly to critical work, collaborating with Credit Risk and Data Science to deploy validated models in production.

You will help improve reliability, latency, and observability across services, while advancing MLOps and deployment automation in a