# Risk Ops Engineer at Indrive

> Hybrid · Cyprus · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/pinpoint%3Aindrive%3A426478 (read 2026-11-08).

## Facts (observed)

- Company: [Indrive](https://alderhq.app/companies/indrive)
- Location text: Cyprus, Limassol
- Where it can be done from (inferred): Hybrid · Cyprus (evidence: "Location: Cyprus, Limassol")
- Posted: already open when tracking began; date unknown
- First seen by alder: 2026-11-04 · last verified: 2026-11-07
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://indrive.pinpointhq.com/en/postings/4bfcf26d-9a07-4a83-89ac-bf4686a9509e
- Skills listed (inferred): Python, Go, MySQL, AWS, Docker, Distributed systems

## Pay and sponsorship

- Posted pay: €76k per year (from the description)

## Description (the employer's text)

We are looking for a hands-on Risks Ops to own and evolve our core Decision-Making System (DMS) and credit processing engine. In this role, you will bridge the gap between risk management, and core engineering, leading a focused team of developers to design, build, and optimize high-throughput, low-latency decisioning engines.

You will play a pivotal role in streamlining time-to-market for credit strategy changes, integrating advanced ML scoring models, and replacing legacy software with modern, scalable, in-house microservices architecture.

### Responsibilities

- Technical Leadership & Ownership: Take full ownership of the end-to-end Decision-Making System (DMS) lifecycle, including system architecture, deployment pipelines, production monitoring, continuous code cleanup, and performance optimization.
- Cross-Functional Collaboration: Partner closely with Risk Analysts and Data Scientists to translate complex business logic, risk rules, and ML scoring models (application, behavioral, fraud) into production-ready software.
- Team Leadership & Delivery: Implement core logic of DMS system to suit the needs of InDrive's lending vertical. Code reviews, release management, and performance evaluations while fostering an efficient, agile environment.
- Process Optimization: Reduce time-to-market for credit strategy updates by modernizing delivery workflows, introducing automated regression/retrospective testing, and eliminating dependencies on legacy release cycles.
- Architecture & System Integration: Drive the evolution of our high-volume microservices ecosystem (Golang, AWS). Integrate internal and external services (credit bureaus, collection platforms, fraud detection services) via REST APIs and event-driven pipelines.
### Skills

- Fintech & Credit Decisioning Expertise: 7+ years of total backend engineering experience, with deep domain expertise in financial technology, credit conveyors, loan processing, debt collection systems, or automated decisioning engines.
- Risk-Ops experience: be familiar with any popular decision-engine (gorules, n8n, camunda, SAS RTDM, FICO Blaze Advisor etc…)
- Stack: Strong expertise in Golang / Python, and build tools (Gitlab CI), AWS-related experience will be a huge plus.
- Data Management: Proficiency with relational and non-relational databases (MySQL, AuroraDB, DynamoDB, GBQ).
- DevOps, CI/CD & Observability: Hands-on experience with modern CI/CD tools (GitLab CI, GitHub Actions), containerization (Docker), and system monitoring tools (Prometheus, Grafana).
### Benefits

- Help us challenge injustice by creating fair choices for millions of people across 47 countries.
- Develop your professional skills with access to mentoring, career consulting, and learning programs.
- Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program.
- Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects.
- Work alongside people who take initiative, speak openly, and challenge themselves to grow.
- Improve your language skills through co-financed courses and internal speaking clubs.
- Annual salary from 75 600 EUR gross.
Final benefits may vary depending on the location.

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/pinpoint%3Aindrive%3A426478 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.