AI Performance Test Architect

Job Title: AI Performance Test Architect
Location: Tampa, FL (Onsite)
Experience: 4-6 years

Mandatory Skills: AI and Automation, AI Performance Test

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, 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 Google Cloud Platform 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).

Required AI/ML & GenAI Skills:
* 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.