Senior Application Support Engineer with 6+ years of experience in SaaS production support and site reliability engineering for business-critical 24x7 systems. Proven expertise in L2/L3 support, incident management, SQL …
Senior Application Support Engineer with 6+ years of experience in SaaS production support and site reliability engineering for business-critical 24x7 systems. Proven expertise in L2/L3 support, incident management, SQL troubleshooting, and root cause analysis, consistently resolving P1/P2 incidents within SLA. Strong track record of improving MTTR, system uptime (99.9%+), and operational efficiency through automation and proactive monitoring. Experienced in leveraging automation and emerging GenAI techniques to enhance incident resolution and operational intelligence.
Delivered L2/L3 client-facing production support for SaaS applications serving 500+ enterprise users, maintaining 99.9%+ uptime.
Supported business-critical workforce management platform ensuring uninterrupted payroll and scheduling operations for enterprise clients.
Ensured uninterrupted workforce operations (payroll, scheduling) by rapidly resolving production incidents in a business-critical environment.
Managed and resolved 20–30+ incidents per week, including P1/P2 critical issues, achieving ¿95% SLA compliance.
Took end-to-end ownership of major incidents, reducing mean time to resolution (MTTR) by 20–25% through efficient triaging and RCA.
Engineered an automation solution for recurring failures (Openfire, DB connectivity), reducing resolution time from 60 minutes to ¡1 minute.
Automated operational workflows using Python and PowerShell, improving efficiency and reducing manual effort by 25%.
Leveraged Claude (LLM) to automate log analysis, incident summaries, and knowledge base documentation, improving troubleshooting speed and consistency.
Reduced recurring production incidents by 15–20% through proactive monitoring, alert tuning, and problem management initiatives.
Troubleshot application, API, and database issues, including backend failures and data inconsistencies.
Performed deep-dive troubleshooting using logs, metrics, and traces (Splunk, Grafana, Datadog), accelerating issue identification and resolution.
Utilized advanced SQL queries for data validation and issue resolution, reducing data-related defects by 30%.
Conducted post-incident RCA and trend analysis, implementing preventive fixes to improve system reliability.
Collaborated with engineering, product, and release teams to resolve defects and ensure smooth production deployments.
Monitored system performance, application health, and capacity metrics, ensuring adherence to SLA/OLA targets.
Managed incident, problem, and change lifecycle via ServiceNow.
Authored runbooks, SOPs, and KB articles, reducing onboarding time by 30%.
Applied AIOps techniques for anomaly detection and incident trend analysis.
Supported cloud-hosted distributed systems, ensuring high availability and performance.
Delivered L2/L3 client-facing production support for SaaS applications serving 500+ enterprise users, maintaining 99.9%+ uptime., Supported business-critical workforce management platform ensuring uninterrupted payroll and scheduling operations for enterprise clients., Ensured uninterrupted workforce operations (payroll, scheduling) by rapidly resolving production incidents in a business-critical environment., Managed and resolved 20–30+ incidents per week, including P1/P2 critical issues, achieving >95% SLA compliance., Took end-to-end ownership of major incidents, reducing mean time to resolution (MTTR) by 20–25% through efficient triaging and RCA., Engineered an automation solution for recurring failures (Openfire, DB connectivity), reducing resolution time from 60 minutes to
Handled 40–60+ customer interactions daily, maintaining high resolution accuracy.
Achieved 90%+ first contact resolution (FCR).
Reduced escalation rate by 15%.
Ensured SLA adherence across high-volume ticket queues.
Handled 40–60+ customer interactions daily, maintaining high resolution accuracy., Achieved 90%+ first contact resolution (FCR)., Reduced escalation rate by 15%., Ensured SLA adherence across high-volume ticket queues.
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