Description

Load Testing Tools:

 Deep expertise in tools such as JMeter, LoadRunner, Gatling, k6, NeoLoad, or BlazeMeter.

APM & Observability: Strong hands-on experience with Datadog, Azure Application Insights, and exposure to Dynatrace, New Relic, AppDynamics, Splunk, Grafana, or Prometheus. Bottleneck Analysis: Expert-level skills in performance bottleneck identification across CPU, memory, threads, GC, database queries, network latency, and microservices. CI/CD Integration: Experience integrating performance testing into pipelines using Jenkins, Azure DevOps, GitHub Actions, or GitLab CI. Cloud Platforms: Working knowledge of AWS, Azure, or GCP — including auto-scaling, load balancing, and cloud-native performance considerations. Scripting & Programming: Proficiency in Java, Python, Groovy, or JavaScript for scripting and automation. Protocols & Architectures: Strong understanding of HTTP/HTTPS, REST/SOAP APIs, WebSockets, microservices, message queues (Kafka, RabbitMQ), and database performance (SQL/NoSQL). AI/ML & GenAI Skills (Required) AI-Powered Observability: Hands-on experience with AIOps platforms and AI-driven APM features such as Datadog Watchdog/Bits AI, Dynatrace Davis AI, New Relic AI, or Azure AI Anomaly Detector. Predictive Performance Analytics: Experience using ML models for capacity forecasting, performance trend analysis, and proactive bottleneck prediction. Anomaly Detection & Root Cause Analysis (RCA): Ability to design or leverage AI/ML models for automated anomaly detection, intelligent alerting, noise reduction, and AI-assisted RCA. Generative AI for Engineering Productivity: Practical experience using GenAI tools (ChatGPT, Copilot, Claude, Gemini) for automated script generation, test data creation, log/trace summarization, and intelligent reporting. Data & ML Foundations: Working knowledge of Python data libraries (Pandas, NumPy, Scikit-learn), time-series analysis, and basic ML concepts applied to performance datasets. Intelligent Test Automation: Familiarity with AI-driven approaches for self-healing test scripts, smart workload modeling, and risk-based performance test selection. Prompt Engineering: Ability to craft effective prompts to integrate LLMs into performance engineering workflows for analysis, recommendations, and automation. Preferred Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field. Industry certifications in performance engineering, cloud platforms (AWS/Azure), APM tools (Datadog, Dynatrace), or AI/ML certifications (Azure AI Engineer, AWS ML Specialty, Google ML Engineer) is a plus. Experience in regulated industries (Financial Services, Healthcare, Insurance) is a plus. Knowledge of chaos engineering, resilience testing, and AI-driven SRE practices. Experience building or integrating custom ML models or LLM-based agents to support performance

Key Skills

Education

Bachelor's or Master's degrees