Senior QA Engineer

Project description We're building a high-throughput analytics platform processing capital markets data with Java microservices, React UI, and OpenAI LLM integrations. Looking for an NFR-focused QA engineer to own performance, reliability, and security at scale. Responsibilities

• What You'll Do
• Drive performance + load testing (APIs, UI, data pipelines, LLM calls)
• Validate P95/P99 latency, throughput, and system scalability
• Test resilience: failover, retries, rate limits, external dependency failures (LLMs, data feeds)
• Identify bottlenecks across microservices, DB, caching, streaming (Kafka)
• Perform LLM NFR validation: latency, fallback, prompt robustness, cost efficiency
• Cover security aspects: auth flows, data protection, prompt injection risks
• Define SLIs/SLOs, improve observability (metrics, logs, tracing)
• Automate NFR checks in CI/CD pipelines SKILLS Must have
• Hands-on experience with performance and load testing tools (k6, JMeter, Gatling) and analysis of P95/P99 latency, throughput, and concurrency Strong understanding of Java-based backend systems (Spring Boot, REST APIs, async processing) and common performance bottlenecks Knowledge of microservices and distributed systems (inter-service communication, retries, circuit breakers, caching) Experience testing client-server web applications, including React frontend performance using browser profiling tools Ability to analyze system metrics (CPU, memory, GC, threads) and identify root causes of performance issues Hands-on experience with observability tools (Prometheus, Grafana, ELK, APM tools) for troubleshooting and monitoring Working knowledge of databases and SQL, including query performance, indexing, and data consistency under load Experience integrating tests into CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps) with automated NFR validation Understanding of security fundamentals (OAuth2/JWT, OWASP risks) and ability to perform basic vulnerability testing Ability to design and execute resilience/failure testing scenarios (timeouts, dependency failures, network issues) Nice to have Experience with capital markets or low-latency data systems (real-time feeds, high-frequency data processing) Familiarity with LLM/OpenAI integrations, including latency, reliability, and prompt-level risk testing Exposure to streaming platforms (Kafka) and event-driven architectures in high-throughput systems