Risk Ops Engineer

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).