Bachelor's Degree in Computer Science, Engineering, Data Science, or related field.
3-5 years experience in DevOps, SRE, Platform Engineering, or operational analytics roles.
Strong understanding of distributed systems, cloud infrastructure, and reliability engineering concepts.
Experience working with monitoring, logging, or observability platforms.
Hands-on scripting or programming experience (e.g., Python, Go, Bash).
Experience analyzing operational datasets such as logs, metrics, or event streams.
Strong analytical thinking and troubleshooting capabilities.
Experience in Payment Gateway, FinTech, Banking, or high-transaction platforms.
Exposure to cloud platforms such as AWS, GCP, or Azure.
Familiarity with container platforms and orchestration environments.
Experience with messaging or event streaming systems.
Knowledge of AI/ML tooling for anomaly detection or predictive analytics.
Experience supporting regulated production environments (e.g., PCI DSS).
Pre-Requisites :
Razer is proud to be an Equal Opportunity Employer. We believe that diverse teams drive better ideas, better products, and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for e
Additional Information
Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is a place to do great work , offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also a great place to work, providing you the unique, gamer-centric #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.
Job Responsibilities :
We are seeking an experienced AIOps Engineer to enhance the reliability, performance, and operational intelligence of mission-critical payment platform infrastructure and services.
This role focuses on designing and implementing intelligent automation solutions, improving observability practices, and leveraging AI-assisted operational capabilities to proactively detect system risks and optimize platform performance. The AIOps Engineer will work closely with DevOps, SRE, and Software Engineering teams to reduce operational overhead, improve incident response efficiency, and support scalable transaction processing environments. Key Responsibilities:
Operational Intelligence & Observability Engineering
Design and improve monitoring strategies covering infrastructure, applications, transaction flows, and distributed system dependencies.
Build dashboards and alerting frameworks that provide actionable operational insights.
Analyze logs, metrics, traces, and telemetry data to identify performance degradation patterns and reliability risks.
Define and track service reliability indicators such as SLIs and SLOs.
Automation & Intelligent Workflow Implementation
Develop automation scripts and workflows to reduce repetitive operational tasks and improve system recovery speed.
Implement intelligent alert enrichment and automated incident triage mechanisms.
Improve signal-to-noise ratio by tuning anomaly detection thresholds and alert correlation logic.
Support creation of self-healing mechanisms for common infrastructure or application failure scenarios.
Incident Response & Reliability Improvement
Participate in incident investigations and lead technical diagnosis for complex operational issues.
Identify systemic reliability weaknesses and recommend engineering improvements.
Reduce Mean Time to Detect (MTTD) and Mean Time to Recover (MTTR) through improved tooling and automation.
Contribute to resilience engineering initiatives such as failover validation or controlled fault simulations.
Performance & Capacity Intelligence
Analyze operational data to forecast infrastructure capacity requirements and scaling thresholds.
Provide recommendations for performance optimization across compute, database, messaging, and networking components.
Support cost efficiency initiatives through workload behavior analysis.
AI-Enabled Operational Innovation
Implement AI use cases such as:
anomaly detection for infrastructure metrics
log clustering and automated incident summarization
predictive scaling signals based on workload patterns
deployment risk analysis using historical operational data
operational insight dashboards for proactive decision-making
Evaluate emerging AIOps tools and integrate suitable capabilities into monitoring and automation platforms.
Collaborate with R&D and platform teams to validate intelligent automation solutions in production environments.
Security & Compliance Awareness
Ensure operational tooling and automation workflows align with secure engineering practices and regulatory expectations.
Support audit readiness by maintaining visibility and traceability of monitoring configurations and operational actions.
Contribute to detection use cases related to abnormal infrastructure or system behavior.
Razer · Shah Alam