Performance Tester Internship

Overview: Scalability and Speed Optimization

The Performance Tester is dedicated to ensuring the speed, stability, and scalability of our entire supply chain e-commerce platform. You will plan, design, and execute rigorous load, stress, and endurance tests on the core e-commerce storefront, the performance-critical internal SaaS tools (OMS, WMS, MES), and the emerging AI agent services to identify and eliminate performance bottlenecks.

Internship Details

Duration: 3 months

Start Date: Immediate

Location: Remote

Stipend: None initially. Based on your first-quarter performance, you may be offered a paid full-time opportunity, or even be absorbed directly by the client as an FTE.

Key Responsibilities & Core Projects

You will be the expert in pushing the platform to its limits, guaranteeing optimal user experience even under peak load.

• Test Strategy & Design: Design comprehensive load, stress, and endurance testing strategies for critical systems, focusing on the high-volume e-commerce checkout funnels, payment gateway integrations, and API responsiveness across the modular monolith.

• Execution & Analysis: Execute tests using industry-standard tools (e.g., JMeter, Gatling) and cloud-based load testing services. Analyze results to accurately identify performance bottlenecks in the Node.js/NestJS services, database queries (PostgreSQL/Redis), and infrastructure components.

• Critical Flow Testing: Specifically test the performance and throughput of key supply chain APIs and user flows, including real-time inventory checks (WMS integration), order placement (OMS), and search functionality (Meilisearch).

• Optimization Recommendations: Collaborate closely with the SRE and development teams to translate performance data into clear, actionable optimization strategies for code, database indices, caching (Redis), and infrastructure (auto-scaling).

• CI/CD Integration: Work with the DevSecOps team to integrate performance testing as a mandatory quality gate within the CI/CD pipeline to prevent performance regressions in new deployments.

Required Technologies & Tools

Candidates must possess deep experience in performance testing methodology, execution, and analysis:

• Testing Tools: Expert proficiency in JMeter, Gatling, or similar high-scale load generation tools.

• Monitoring & Analysis: Hands-on experience using the observability stack (Prometheus, Grafana, OpenTelemetry) to monitor system metrics, latency, and resource utilization during test execution.

• Programming/Scripting: Proficiency in scripting languages (e.g., JavaScript/Python) for test scenario creation and data generation.

• Backend Knowledge: Strong understanding of database performance tuning (PostgreSQL), caching, and API behavior (REST/JSON).

• Cloud/Infra: Familiarity with cloud-based load testing services and containerized environments (Docker).

AI Agent Focus

You will validate the capacity and latency of our AI services.

• Load Testing AI: Conduct stress and load tests on the AI agent systems, measuring latency and throughput under concurrent user requests, especially concerning the processing power required for LLM interactions (e.g., prompt processing, RAG retrieval).

• Resource Impact: Analyze the impact of AI compute demands on shared infrastructure resources, providing data for cloud capacity planning.

Success Metrics & Career Path

Performance will be measured by:

• Latency Reduction: Measurable improvements in API response times (e.g., 95th percentile latency) for critical services.

• Throughput: Successful validation of system throughput capabilities against business-defined peak load targets.

• Bottleneck Identification: Accuracy and impact of identified performance bottlenecks and recommended solutions.

Mentorship Structure: Reports to the Automation QA Lead or Technical Architect, working directly with the SRE and Backend Engineering teams to ensure performance is a core product feature.

Originally posted on Himalayas