Engineering Lead (Credit Risk)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 11d ago
The date the source published, not the day we noticed it (2026-09-29). Last seen at its source 2h ago.
We have tracked this listing since 5 Oct 2026 (5 days). The employer's own board has carried it every time we have read it, most recently 2 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Argentina, Santo Domingo, Colombia
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $4,300–7,062/mo
Middle 50% of 20 listings that do state pay — Engineering · all levels · Argentina · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Ashby employer's own board first seen 5d ago · last seen 2h ago
The listing
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.