Sr. Drools Developer

We are seeking a Senior Drools Engineer to design, build, and maintain business rules for a large‑scale underwriting and decisioning platform. This role focuses on translating complex credit and risk policies into high‑performance, explainable Drools rules that power real‑time decisions. Key Responsibilities • Design, develop, and maintain Drools (KIE) rules using DRL, Decision Tables, and Rule Templates to implement complex underwriting, eligibility, and policy logic • Optimize rule execution for low latency and high throughput , including management of rule salience, agenda groups, rule flows, and conflict resolution • Ensure decisions are deterministic, explainable, and auditable , meeting regulatory and audit requirements • Contribute to the overall decisioning and rules architecture , designing rule sets for reusability, versioning, and controlled rollout • Participate in design reviews and technical discussions related to rule design and platform evolution • Develop unit tests and scenario‑based tests to validate rule behavior across multiple decision paths • Troubleshoot production issues related to rule execution and support deployments across development, QA, and production environments • Act as a subject‑matter expert for Drools and business rules best practices, mentoring junior engineers • Establish and enforce standards for rule authoring, documentation, testing, and maintainability Required Qualifications • 7+ years of software engineering experience • 5+ years hands‑on experience with Drools / KIE • Strong proficiency in Java • Experience in building a lightweight orchestration layer using Apache Pekko is a plus • Deep experience with: ◦ DRL, Decision Tables, Rule Templates ◦ Stateless and stateful KIE sessions ◦ Rule performance tuning and debugging ◦ Experience building or supporting policy‑driven or decisioning systems ◦ Strong understanding of business rules management systems (BRMS) ◦ Exposure to cloud platforms and CI/CD pipelines What Success Looks Like • Business policies are accurately captured as maintainable Drools rules • Decisions are fast, consistent, and explainable • Rule changes can be made confidently without impacting platform stability • The rules layer is reliable, scalable, and regulator‑ready