Engineering Lead (Credit Risk)

Kiwi Financial Inc.; Argentina; Hybrid; ARS 197,065,000 - 257,701,000; Full time

Job summary

Kiwi Financial Inc. is seeking a hands-on Engineering Lead to guide the team building Kiwi's credit risk capabilities within our lending products.

You will work across application services, data, risk APIs, and ML models to ensure reliable production systems and fast delivery of improvements. You will lead engineers and stay involved in design, implementation, reviews, and production support, collaborating with Credit Risk and Data Science to integrate validated models and improve MLOps across

Qualifications

• Experience leading engineers while staying hands-on in system design and production support.
• Strong backend expertise with TypeScript/Node.js, building and operating APIs and microservices.
• Experience with MLOps in production, including deployment, monitoring and rollback.
• Python knowledge and ML-model serving in production.
• AWS deployment of containerized services with CI/CD pipelines.

Responsibilities

• Lead the Credit Risk Engineering team, setting technical direction, planning delivery, mentoring engineers, reviewing code, and contributing hands-on to critical work.
• Design, build, and operate the backend services and APIs that integrate risk capabilities into Kiwi's lending products.
• Improve the reliability of credit decision flows across services and external dependencies, including API contracts, latency, timeouts, failure handling, observability, and incident response.
• Partner with Credit Risk and Data Science to integrate models into production and improve MLOps practices, including deployment automation, versioning, testing, monitoring, and rollback.
• Improve the architecture and maintainability of risk services and integrations, addressing technical debt while supporting the delivery of new risk capabilities.

Skills

Hands-on leadership

Backend engineering

System design

Cross-team collaboration

Technical judgment

MLOps knowledge

Python

Tools

TypeScript

Node.js

AWS

Docker

CI/CD

PostgreSQL

Snowflake

FastAPI

LightGBM

scikit-learn

dbt

GitHub Actions

Job description

We are looking for a hands-on Engineering Lead to lead the engineering team responsible for bringing Kiwi's credit risk capabilities into our lending products.

Credit decisions depend on multiple systems working together: application services, data and external providers, risk APIs, and machine learning models. This team builds and operates the software that connects those systems and supports decisions throughout the lending lifecycle. Its work has a direct impact on the reliability of our credit application flow and the speed at which we can deliver improvements.

You will lead engineers and remain actively involved in technical design, implementation, code reviews, and production support. You will work closely with Credit Risk and Data Science to integrate validated models into reliable production systems and improve the engineering practices that support their deployment and operation.

Responsibilities

• Lead the Credit Risk Engineering team, setting technical direction, planning delivery, mentoring engineers, reviewing code, and contributing hands-on to critical work.
• Design, build, and operate the backend services and APIs that integrate risk capabilities into Kiwi's lending products.
• Improve the reliability of credit decision flows across services and external dependencies, including API contracts, latency, timeouts, failure handling, observability, and incident response.
• Partner with Credit Risk and Data Science to integrate models into production and improve MLOps practices, including deployment automation, versioning, testing, monitoring, and rollback.
• Improve the architecture and maintainability of risk services and integrations, addressing technical debt while supporting the delivery of new risk capabilities.

Requirements

• Experience leading engineers while remaining hands-on with system design, implementation, and production support.
• Strong backend engineering experience with TypeScript and Node.js, including building and operating APIs and microservices.
• Experience integrating services into critical customer flows, with a solid understanding of latency, timeouts, partial failures, retries, and observability.
• Proven hands-on experience with MLOps in production, including model deployment, versioning, monitoring, and rollback.
• Working knowledge of Python and experience collaborating on systems that serve machine learning models in production.
• Experience deploying and operating containerized services on AWS, using Docker and CI/CD pipelines.
• Strong SQL and PostgreSQL skills, including investigating data quality and production issues.
• Strong technical judgment and the ability to work effectively across Engineering, Credit Risk, and Data Science.

Our technology

Our lending platform uses TypeScript, Node.js, PostgreSQL, Docker, GitHub Actions, and AWS. Our risk and data science environment includes Python, FastAPI, LightGBM, scikit-learn, Snowflake, Airflow, and dbt. We value experience with the underlying engineering challenges; prior use of every tool in this stack is not required.

Nice to have

• Experience with consumer lending or BNPL products.
• Experience building or integrating with credit decision engines or rules-based systems.
• Familiarity with Snowflake or a comparable analytical data platform.
• Familiarity with model governance and explainability in US consumer lending.

What we offer

• The opportunity to work on critical financial products with direct impact on customers and business growth.
• High technical ownership and the opportunity to shape how Kiwi's credit risk technology evolves.
• Meaningful challenges across backend architecture, production integrations, reliability, and MLOps.
• An engineering environment where AI is becoming a core part of how we build software.
• A collaborative multidisciplinary team across Engineering, Credit Risk, Data Science, Product, QA, and Platform.
• 100% remote — Argentina, the Dominican Republic, or Colombia.