AI Performance Test Architect in Tampa
Energy Jobline ZR · Tampa, US ·
- Category
- Architecture
Energy Jobline ZR · Tampa, US ·
Job Description
Technical Skills
Load Testing Tools: Deep expertise in 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 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- 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 such as ChatGPT, Copilot, Claude, or Gemini for automated script , test data creation, log/trace summarization, and intelligent reporting.
Data & ML Foundations: Working knowledge of Python data libraries such as Pandas, NumPy, and Scikit-learn, along with 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.
Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a 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) are a plus.
Experience in regulated industries such as Financial Services, Healthcare, or 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.