Bachelor's Degree in Computer Science, Engineering, Data Science, or related field.
Minimum 5 years experience in DevOps, SRE, Platform Engineering, or Operational Analytics roles.
Strong understanding of distributed systems, cloud infrastructure, and reliability engineering principles.
Experience working with monitoring and observability platforms.
Familiarity with scripting or programming languages such as Python, Go, or Bash.
Experience analyzing operational data such as logs, metrics, or event streams.
Strong troubleshooting, analytical, and problem-solving skills.
Experience in Payment Gateway, FinTech, Banking, or high-transaction systems.
Exposure to cloud platforms such as AWS, GCP, or Azure.
Experience with container platforms and orchestration environments.
Familiarity with messaging systems or event streaming architectures.
Knowledge of AI/ML tooling for anomaly detection, pattern recognition, or predictive analytics.
Experience supporting regulated environments such as PCI DSS.
Pre-Requisites :
Razer is proud to be an Equal Opportunity Employer. We believe that dive
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 Senior AIOps Engineer to enhance the reliability, scalability, and operational intelligence of mission-critical payment platform infrastructure and services.
This role focuses on leveraging automation, advanced analytics, and AI-driven operational tooling to improve system observability, incident response efficiency, performance optimization, and proactive risk detection across high-throughput transaction processing environments.
The successful candidate will work closely with DevOps, SRE, Engineering, and Platform teams to design intelligent operational workflows that reduce manual intervention, improve service availability, and support continuous platform growth. Key Responsibilities:
AIOps Platform Development & Automation
Design and implement intelligent automation solutions to improve operational efficiency and reduce repetitive infrastructure and application support tasks.
Develop tools and pipelines for automated incident triage, alert enrichment, and operational diagnostics.
Integrate AI/ML capabilities into monitoring, logging, and event management platforms.
Improve signal-to-noise ratio by optimizing alerting strategies and anomaly detection mechanisms.
Other duties as assigned.
Observability Engineering & Operational Intelligence
Enhance monitoring frameworks covering infrastructure, applications, transaction flows, and distributed system dependencies.
Build intelligent dashboards and predictive insights to support proactive reliability management.
Analyze large-scale operational datasets including logs, metrics, traces, and transaction telemetry.
Define and track SLIs, SLOs, and reliability indicators for critical payment services.
Incident Prediction & Reliability Optimization
Implement predictive models and heuristics to identify early indicators of system degradation or failure.
Collaborate with SRE and platform teams to automate remediation workflows and self-healing mechanisms.
Reduce Mean Time to Detect (MTTD) and Mean Time to Recover (MTTR) through intelligent automation and operational playbooks.
Contribute to resilience engineering initiatives including chaos testing and reliability simulations.
Platform Performance & Capacity Intelligence
Develop analytics to forecast workload growth, capacity requirements, and scaling thresholds.
Provide recommendations for infrastructure tuning, cost efficiency, and performance optimization.
Support engineering teams in identifying performance bottlenecks across compute, database, messaging, and network layers.
Security, Compliance & Governance Support
Ensure AI-driven operational tooling aligns with secure engineering practices and regulated environment requirements.
Support audit readiness through improved operational visibility and traceability.
Contribute to anomaly detection use cases related to infrastructure misuse or unusual operational patterns.
AI Innovation & Research Collaboration
Evaluate emerging AIOps tools, frameworks, and techniques for suitability in high-availability payment environments.
Prototype intelligent operational capabilities such as:
predictive incident correlation
automated runbook execution
intelligent deployment risk analysis
log summarization and pattern clustering
transaction degradation early-warning signals
Promote responsible AI adoption and knowledge sharing across engineering teams.
Razer · Shah Alam