Real-Time Payments Engineer III: Low-Latency Microservices

JPMorgan Chase & Co.

Singapore

On-site

SGD 120,000 - 180,000

Full time

14 days+

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Job summary

JPMorgan Chase & Co. in Singapore seeks a Software Engineer III for Real-Time Payments Services to build and run mission-critical RTP services with ownership over engineering, resiliency, release readiness, and incident response.

You will work with GemFire/Geode, microservices, cloud-native architecture on AWS, and contribute to secure, compliant payment outcomes while improving code quality through AI-assisted engineering practices.

Qualifications

Formal training or certification on software engineering concepts and 3+ years applied experience.
Bachelor’s Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
Strong Java ecosystem skills (Java 11+/Spring Boot), REST/gRPC APIs, asynchronous/event-driven design.
Proven experience with microservices architecture and domain-driven service decomposition.
Hands-on experience with GemFire/Geode (regions, partitioning, WAN replication, CQ/events, consistency/performance tuning).
Strong AWS experience: EKS/ECS, EC2, VPC, ALB/NLB, S3, RDS/Aurora, ElastiCache, IAM, CloudWatch, KMS, Secrets Manager.
Experience with cloud-native operations: containers, Kubernetes, autoscaling, health checks, service mesh (preferred).
Solid incident management background in high‑availability systems, including on‑call, runbooks, SRE practices, and post‑incident remediation.
Messaging and streaming experience (Kafka, MQ, SNS/SQS) and resilient integration patterns.
Strong understanding of security and compliance in financial systems (encryption, tokenization, least privilege, audit controls).
Demonstrated experience leading effective use of approved AI‑assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Responsibilities

Lead production incident management for payment flows (P1/P2), including triage, war-room coordination, mitigation, RCA, and preventive actions.
Develop and scale microservices for payment initiation, validation, routing, settlement, reconciliation, and exception handling.
Implement distributed in-memory data patterns using VMware GemFire / Apache Geode for ultra-low-latency state, reference data, and session/context caching.
Build AI-assisted observability and operations use cases (anomaly detection, alert correlation, incident prediction, runbook automation, GenAI-assisted triage).
Own release quality with CI/CD gates, canary/blue-green strategies, rollback automation, and zero/low-downtime deployments.
Collaborate with product, operations, risk, compliance, and partner banks/processors to deliver secure and compliant payment outcomes.
Ensure strong controls for audit/regulatory needs, data lineage, traceability, and operational reporting.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root‑cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.