AI Performance Test Architect

Flexon Technologies Talent360.ai; Tampa (FL); On-site; USD 150,000 - 210,000; Full time

Job summary

Flexon Technologies Talent360.ai seeks a seasoned performance engineer to lead load testing and observability initiatives across cloud platforms. You will apply AI/ML for capacity forecasting, anomaly detection, and proactive bottleneck prediction, while integrating tests into CI/CD pipelines.

Ideal candidates have deep experience with JMeter, Datadog, Dynatrace, and GenAI tooling, with a background in Python data libraries and AI-driven SRE practices.

Qualifications

• Experience with large-scale load testing and performance engineering.
• Hands-on with cloud platforms and observability tools.
• Proficiency in ML/AI techniques applied to capacity planning.

Skills

Load testing tools

APM & Observability

AI/ML for performance

GenAI tooling

Cloud platforms

CI/CD integration

APIs & web protocols

Python data libraries

Anomaly detection

Prompt engineering

Education

Bachelor's or Master's in CS/Engineering/Data Science

Tools

JMeter

LoadRunner

Gatling

k6

NeoLoad

BlazeMeter

Datadog

Azure Application Insights

Dynatrace

New Relic

AppDynamics

Splunk

Grafana

Prometheus

Kafka

RabbitMQ

Job description

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-native performance considerations.

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 generation, 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.

Preferred 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.