Performance Test Architect

Performance Test Architect

Tampa, FL - Onsite

Contract

Technical Skills

• 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 engineering workflows.