Performance & Scalability Engineer – Custom Tooling Development

Role Overview We are seeking a hands-on Performance and Scalability Engineer to own performance characterization, workload modelling, bottleneck analysis, scalability testing, and custom performance-tool development for cloud-native and distributed applications. The ideal candidate will go beyond executing load tests and be capable of developing reusable engineering tools, analysing system behaviour across multiple layers, and working directly with architects and development teams to resolve performance and reliability challenges. Key Responsibilities Define and execute performance, load, stress, endurance, and scalability testing strategies. Establish performance benchmarks and measure latency, throughput, concurrency, resource consumption, and system stability. Identify bottlenecks across application code, APIs, databases, messaging systems, infrastructure, and external dependencies. Design and develop custom performance-testing tools, workload generators, test harnesses, and automation utilities. Build realistic workload models and synthetic data or event generators to simulate production-scale scenarios. Evaluate system behaviour under peak load, sustained traffic, high concurrency, and resource constraints. Perform profiling, telemetry analysis, distributed tracing, and root-cause investigation. Assess cloud application performance and scalability across Azure and, where applicable, GCP. Validate autoscaling, caching, load balancing, failover, and resource-management strategies. Integrate performance testing into CI/CD pipelines and establish repeatable performance regression testing. Work with architecture, development, SDET, and DevOps teams to resolve bottlenecks and improve system efficiency. Produce clear technical reports with findings, performance risks, recommendations, and remediation priorities. Required Skills & Experience Strong software engineering and scripting skills in Python, Java, Go, or a comparable language. Practical experience with performance testing, workload generation, profiling, and scalability analysis. Experience developing custom testing tools, automation frameworks, or performance harnesses. Strong understanding of distributed systems, APIs, databases, networking, concurrency, and resource management. Experience interpreting metrics, logs, traces, and system telemetry to diagnose performance issues. Familiarity with cloud-native applications, containers, and Kubernetes. Ability to translate test results into actionable architectural and implementation improvements. Preferred Tools & Technologies Performance testing: k6, Apache JMeter, Gatling, or LoadRunner. Profiling and diagnostics: language-specific profilers, browser developer tools, distributed tracing, and diagnostic utilities. Observability: Prometheus, Grafana, OpenTelemetry, and centralized logging. Cloud: Microsoft Azure; GCP exposure is beneficial. Automation: Python or other scripting languages, CI/CD pipelines, and infrastructure automation. Distributed workloads: messaging systems, event-driven architectures, and high-volume data-processing systems. Preferred Qualifications Experience testing AI-powered applications, inference APIs, or agentic workflows under concurrent workloads. Experience with chaos engineering, fault injection, or resilience validation. Knowledge of cloud cost and resource optimization. Exposure to FedRAMP or other regulated environments, where relevant. Success Criteria Accurate performance baselines and repeatable scalability testing. Effective identification and resolution of performance bottlenecks. Reusable custom tooling that improves testing efficiency and coverage. Improved application throughput, latency, reliability, and resource utilization.