inDrive

Global ride-hailing platform with fare negotiation

Risk Operations Engineer

Backend EngineeringUpdated on Nov 13

Full-timeLimassol, CyprusNo salary listedHybrid

About the Job

Requirements

At least 7 years of total backend engineering experience.
Deep domain expertise in financial technology, credit processing, loan processing, debt collection systems, or automated decisioning engines.
Familiarity with a popular decision engine such as GoRules, n8n, Camunda, SAS RTDM, or FICO Blaze Advisor.
Strong expertise in Golang and Python; AWS-related experience is a plus.
Proficiency with relational and non-relational databases, including MySQL, AuroraDB, DynamoDB, or Google BigQuery.
Hands-on experience with CI/CD tools such as GitLab CI or GitHub Actions, Docker, and monitoring tools such as Prometheus or Grafana.

Responsibilities

Own and evolve the end-to-end Decision-Making System (DMS) lifecycle, including system architecture, deployment pipelines, production monitoring, code cleanup, and performance optimization.
Partner with Risk Analysts and Data Scientists to translate business logic, risk rules, and application, behavioral, and fraud scoring models into production-ready software.
Implement core DMS logic for the lending vertical; conduct code reviews, manage releases, and perform performance evaluations while fostering an efficient, agile environment.
Reduce time-to-market for credit strategy updates by modernizing delivery workflows, introducing automated regression and retrospective testing, and eliminating dependencies on legacy release cycles.
Drive the evolution of a high-volume microservices ecosystem using Golang and AWS, integrating internal and external services such as credit bureaus, collection platforms, and fraud detection services through REST APIs and event-driven pipelines.

Desired Qualifications

AWS-related experience.

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.

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.

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).

About the Company

inDrive

Global ride-hailing platform with fare negotiation

inDrive operates a global mobility and urban services platform that connects passengers with independent drivers through a fare-negotiation system. Riders propose a trip price, and nearby drivers can accept, decline, or counteroffer, creating a peer-to-peer ride-hailing model. The service has expanded to intercity rides, delivery and cargo, groceries, advertising, and driver-focused fintech within its app. Unlike typical ride-hailing apps that use fixed fares, inDrive enables price negotiation and direct driver-rider interaction, and it pursues growth through a venture arm that funds ride-hailing-adjacent startups in emerging markets. This broader strategy aims to build a super-app for urban and gig-economy needs, with the goal of offering a wide range of mobility and urban services in 48 countries.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $532M
Headquarters: Mountain View, CA
Founded: 2012

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