Performance Test Engineer

Innominds Software; Bengaluru; Hybrid; INR 1,500,000 - 1,900,000; Full time

Job summary

Innominds Software in Bengaluru is seeking an experienced Performance Engineer to design and evolve scalable testing frameworks and drive end-to-end performance across applications, APIs, and cloud-native services.

You will work with Kubernetes-based microservices, implement observability with Prometheus, Grafana, OpenTelemetry, and partner with DevOps and Cloud teams to optimize capacity and reliability.

Qualifications

• 10+ years of experience in Performance Engineering.
• Strong hands-on expertise across applications, APIs, JVM, databases, Linux, cloud.
• Experience with Kubernetes-based microservices and observability tools.

Responsibilities

• Design and evolve reusable performance engineering frameworks for load, stress, spike, endurance, and scalability testing using JMeter, Python, and Bash.
• Analyze protocol-level performance scripting for HTTP/HTTPS, REST APIs, WebSocket, TCP/IP, and JDBC including correlation and authentication.
• Work with Kubernetes-based microservices, monitoring pod/container metrics, HPA, and service communication.
• Implement observability with Prometheus, Grafana, OpenTelemetry, dashboards and alerts.
• Perform end-to-end performance analysis and root-cause identification across microservices, JVM, DB, Linux, and network.
• Drive CI/CD performance integration using GitHub, GitHub Actions, Jenkins, AWS/Azure/GCP.
• Provide technical leadership and mentor engineers in performance engineering practices.
• Exposure to AI/ML techniques for anomaly detection or test-result triage.
• Strong client-facing communication skills for presenting findings to stakeholders.

Job description

Role & responsibilities

Experience

10+ years of experience in Performance Engineering, with strong hands-on expertise across applications, APIs, JVM, databases, Linux, cloud, Kubernetes, microservices, and distributed environments.

Location

India / Bangalore (Whitefield)

Employment Type

Full-time

Job Summary

• Design and evolve reusable Performance Engineering frameworks covering load, stress, spike, endurance, scalability, capacity, failover, and resilience testing, using JMeter, Python/pytest, and Bash.
• Strong hands-on experience in protocol-level performance scripting using HTTP/HTTPS, REST APIs, WebSocket, TCP/IP, and JDBC, including correlation, parameterization, authentication, session/connection handling, payload manipulation, persistent/concurrent connections, and protocol troubleshooting.
• Hands-on experience with Kubernetes-based microservices, including pod/container/node metrics, resource requests/limits, CPU/memory throttling, scaling, HPA, pod restarts, service communication, and application performance.
• Strong expertise in observability and performance monitoring, including installation/configuration of Prometheus, Grafana, and OpenTelemetry, with dashboards, alerts, and correlation of metrics, logs, and traces.
• Ability to perform end-to-end performance analysis and root-cause identification by correlating microservices, Kubernetes, JVM, database, Linux, network, load balancer, and infrastructure metrics.
• Strong hands-on experience in JVM and database performance engineering, covering GC, heap, threads, CPU/memory, Metaspace, JIT/code cache, query latency, TPS, connections, locks/waits, execution plans, indexing, and I/O.
• Derive production-based workload models from traffic, logs, monitoring data, API statistics, transaction volumes, peak-hour patterns, concurrency, and business usage, and convert them into realistic scalable performance workloads.
• Integrate Performance Engineering into CI/CD pipelines using GitHub, GitHub Actions, and Jenkins, including automated execution, reporting, SLA/SLO validation, performance regression detection, and quality gates; experience with AWS/Azure/GCP.
• Provide technical leadership and hands-on troubleshooting across the complete performance stack, drive capacity/scalability and resilience engineering, collaborate with Development, DevOps, Cloud, Database, and Infrastructure teams, and mentor engineers on modern Performance Engineering practices.
• Solid understanding of endpoint or OS-level performance testing, ideally with exposure to VDI, or endpoint-management platforms
• Exposure to applying AI/ML techniques for performance anomaly detection, predictive capacity analysis, or automated test-result triage.
• Strong client-facing communication skills comfortable presenting performance findings and recommendations directly to client stakeholders.