Aditya Chavali

Overall Summary

• Played a pivotal role in improving production monitoring maturity, leading to better visibility, more accurate alerting, and increased platform reliability during large-scale releases.
• Managed and monitored installed systems for highest level of availability.
• Improved secret rotation efficiency by 60% via Python-based Vault automation, reducing manual intervention, and compliance risk.
• Enabled safe, controlled deployments across three major platforms through robust CI/CD integrations and advanced deployment strategies (Canary, Blue/Green).

Deployment Automation and Release Engineering.

• Led production deployments using Ansible, partnering with DevOps teams to enable Canary and Blue/Green deployment models, with dynamic traffic routing via F5 and AVI load balancers.
• Orchestrated seamless deployments across diverse platforms (legacy VMs, PCF, and OpenShift) using tools such as UDeploy and Harness.
• Contributed key insights to enterprise-wide CI/CD initiatives, ensuring pipeline security, resilience, and cross-platform compatibility.

Platform Modernization and Infrastructure Migration.

• Drove F5 to AVI migration efforts for multiple applications, spanning both traditional infrastructure and PCF-based microservices, improving performance and simplifying the load balancing strategy.
• Developed Python-based automation to streamline HashiCorp Vault secret management, improving operational efficiency by 60%, and reducing human error in sensitive configurations.

Observability, Monitoring, and Performance Engineering

• Created real-time Grafana dashboards using data from Splunk to monitor traffic distribution and ensure migration success for critical application flows.
• Drove ongoing improvements in production monitoring and alerting for Line-of-Business (LOB) applications, significantly enhancing end-to-end visibility.
• Developed runbooks, on-call playbooks, and real-time dashboards to support high-visibility national product launches, ensuring incident readiness and stakeholder communication.

AI-Based Alerting and Monitoring Consolidation

• Currently leading a major initiative to consolidate fragmented monitoring systems and implement AI-based alerting.
• Aimed at reducing noise and improving alert accuracy across enterprise applications.

Performance Engineering Leadership.

• Acted as a key contributor across all performance engineering domains, including monitoring, alerting, and tuning, using tools like AppDynamics, Dynatrace, Glassbox, Splunk, ThousandEyes, and General Nelson.
• Collaborated cross-functionally to ensure platform engineering capabilities (resilience, scalability, availability) were fully implemented across mission-critical applications.

Overall Summary

• Spearheaded the automation of AppDynamics onboarding, leading to a 6× increase in APM adoption — scaling from approximately 100 to over 600 applications onboarded, including sub-components and supporting services.
• Observed a 30% improvement in Mean Time to Detect (MTTD) by addressing key observability gaps across the application, infrastructure, and middleware layers.
• Contributed to a significant reduction in Mean Time to Resolve (MTTR) for critical incidents through enhanced monitoring visibility, root cause triage, and proactive escalation support.
• While incident volume remained consistent, improved observability enabled earlier detection of performance degradations, preventing escalation to P1/P2 incidents, and improving service resilience.

APM Strategy and Cross-Application Enablement.

• Designed tailored APM strategies based on individual application architectures; partnered with development teams to guide monitoring adoption, identify feasibility, and resolve implementation gaps.
• Developed standardized monitoring blueprints, including agent selection (Java, infrastructure, DB), business transaction detection, custom alerting thresholds, and anomaly detection setup.
• Provided structured feedback to the AppDynamics product team, leading to enhancements in Java Agent observability for enterprise-grade deployments.

Automation of AppDynamics onboarding

• Architected and implemented an automated AppDynamics onboarding platform using the Spring Framework and MongoDB, reducing onboarding time from 20 minutes to under 3 minutes, significantly boosting adoption across Cisco IT.
• The automation framework was adopted as a reference API design by the AppDynamics product team for wider use cases.

Integration with event correlation and incident management.

• Developed an alert-forwarding system to push AppDynamics events to a Kafka-based event correlation engine, enabling seamless integration with ServiceNow for automated incident creation and triaging.

APM Agent Enhancements and Product-Level Improvements

• Played a key role in enhancing APM agent capabilities by identifying platform-specific gaps and providing structured feedback to AppDynamics product teams, resulting in improved agent observability and performance.
• Led monitoring enablement across a wide range of agents, including: Cluster Agent (OpenShift monitoring), Network Agent (network telemetry to complement APM), Web Server Agent, Database Agents (Oracle, MongoDB, PostgreSQL, Cassandra), Machine Agent with custom extensions (Kafka, RabbitMQ), and Language-specific Agents (Java, Python, Node.js).
• Conducted deep feasibility studies and PoCs to validate agent functionality, enrich metric collection, and standardize onboarding across diverse environments.
• Influenced multiple agent roadmap improvements through recurring engagements with AppDynamics engineering teams.

AI Collaboration and Automation Bots

• Partnered with the TCS internal AI team to develop a proof-of-value 'Smart Sensor' for predictive analytics, leveraging historical APM and performance telemetry.
• Designed and deployed a command center bot capable of triggering remediation actions, such as auto-scaling, server restarts, and traffic rerouting, based on monitoring insights.

Cloud-Native Transformation & CI/CD Integration.

• Led the transformation of internal platform applications from traditional monolithic architectures to cloud-native, containerized solutions deployed on the OpenShift Container Platform (OCP).
• Partnered with enterprise DevOps teams to integrate applications into standardized CI/CD pipelines, ensuring smooth, automated deployment workflows, aligned with organizational best practices.
• Enabled platform services to adopt modern development patterns, improving scalability, fault tolerance, and deployment consistency.
• Ensured cloud-native apps adhered to platform observability, security, and compliance standards as part of the modernization effort.

Incident Management and Root Cause Analysis

• Actively contributed to P1/P2 incident triaging and on-call rotations; leveraged AppDynamics and performance engineering techniques to conduct RCA, and provide both short-term remediations and long-term recommendations to development teams.

Documentation, enablement, and standards.

• Authored comprehensive documentation and playbooks covering APM agent onboarding, alert configuration, performance thresholds, and anomaly detection best practices.
• Established reusable templates and reporting formats to drive consistency in problem detection, observability coverage, and monitoring metrics reporting across teams.

Operational Support for Kubernetes and OpenShift

• For a focused six-month period, I supported the daily health, stability, and availability of critical production Kubernetes clusters, ensuring the seamless operation of containerized applications.
• Led incident management efforts for a wide range of issues, including application deployment failures, YAML misconfigurations, GSLB-related outages, access control, network policies, container crashes, and autoscaling challenges.
• Supported the rollout of OpenShift as a Service, enabling development teams to provision target environments through automated, self-service workflows, significantly reducing onboarding and provisioning effort.

• Engineered and developed comprehensive monitoring strategies for complex application architectures, ensuring high availability and faster response times for live applications.
• Continuously evaluated and integrated multiple monitoring tools to enrich end-to-end performance visibility.
• Performed advanced Java Heap Sizing, Garbage Collection tuning, and Memory leak analysis for microservice-based applications running on Kubernetes clusters.
• Diagnosed and resolved high response time and thread contention issues using APM tools such as Dynatrace, Appdynamics, and New Relic across various application servers including Tomcat, JBoss, and WebLogic.
• Provided recommendations for fine-tuning container-based deployments, including capacity planning, resource sizing, and load balancing strategies.
• Resolved critical P1/P2 production performance issues and collaborated with development teams on design changes to resolve performance bottlenecks.
• Mentored and trained a team of engineers on new technologies and skill enrichment, driving a culture of continuous improvement.

• Led performance tuning for Oracle BPM applications, diagnosing issues like memory leaks using JProfiler, Eclipse MAT, and Samurai.
• Engineered production-like multi-tenant environments on Linux, automating installations and monitoring with custom shell scripts.
• Developed load test scripts with Oracle Application Testing Suite to benchmark key business flows and identify performance bottlenecks.
• Collaborated with DBAs to analyze database performance via AWR and STS reports, improving system efficiency.
• Implemented proactive monitoring strategies to enhance system resilience and streamline deployments.
• Built and proposed prototypes to resolve critical performance issues, while also providing technical guidance and training to junior team members.

• Drove a 48% improvement in application performance by redesigning a legacy Telecom Application MMS Connector with Java and Spring Framework.
• Architected new middleware layer using Core Java and REST APIs, enhancing Telecom MMS Connector functionality.
• Built performance workload model to validate REST API migration success, demonstrating 48% application performance increase.
• Implemented proactive monitoring strategies to ensure high availability in hybrid cloud environments.
• Collaborated with cross-functional teams to conduct root cause analyses and optimize system resilience.

• Architected and developed comprehensive monitoring software solutions for VMware infrastructure, enhancing operational visibility and control.
• Implemented multithreaded Auto-Discovery Engine using Core Java to automate discovery of virtual machines and hosts.
• Designed VMotion Detector monitoring adapter to precisely track virtual machine migrations across infrastructure.
• Developed solutions for Host Bus Adapters (HBA) and Host Data Stores, enabling proactive identification of hardware and storage issues.
• Executed end-to-end performance tests, ensuring robust application performance through monitoring modules.
• Conducted performance tuning and optimization initiatives pre-launch, contributing to a seamless production environment.
• Led cross-functional collaboration to drive root cause analysis and implement proactive monitoring strategies.
• Utilized APM tools and DevOps practices to enhance system resilience in hybrid cloud environments.

• Engineered and maintained production-like lab environments using Oracle Fusion Middleware Suite and Linux servers for robust performance testing.
• Drove performance tuning and optimization for Oracle Application Development Framework (ADF) applications, achieving a 45% improvement in responsiveness.
• Authored custom load test scripts with Load Runner to benchmark critical business flows against production-scale infrastructure.
• Conducted root cause analysis of performance degradation issues, reducing system downtime by 20% through targeted optimizations.
• Implemented proactive monitoring strategies with APM tools to enhance system resilience and ensure high availability.