Performance Testing Engineer -AI

Impact you will realize: Job Responsibilities

• This role can significantly elevate a candidate's profile by positioning them as a technical leader at the intersection of performance engineering and AI, a rare and highly sought-after combination.
• It offers deep exposure to cutting-edge AI systems, including chatbots and model endpoints, which adds a future-proof dimension to their experience. Leading performance strategy across Go microservices and Kubernetes strengthens their credentials in modern distributed architecture.
• Working with tools like Locust, Grafana, Datadog, and Splunk sharpens their observability and automation expertise, making them more marketable. Cross-functional collaboration with Engineering, QE, DevOps, and AI/ML teams builds leadership and influence skills that employers value.
• Integrating performance testing into CI/CD demonstrates DevOps maturity and end-to-end ownership. Documenting findings and driving measurable improvements showcases impact and communication ability, key for senior roles.
• Experience with cloud platforms and AI/ML systems opens doors to high-paying roles in AI-first companies.
• The technical leadership title signals career progression toward principal or architect-level positions.
• Overall, it transforms the candidate into a versatile, high-impact performance leader with a distinctive AI specialization.

Key skills you will require:

Primary Skills

• Performance Strategy & Execution: Design and execute comprehensive load, stress, soak, and UI performance testing strategies for AI features, including intelligent chatbots and automated issue resolution.
• Real-World Traffic Simulation: Simulate realistic, production-like traffic against AI model endpoints using Locust and other load-testing tools.
• Test Automation: Build and maintain robust Python-based test automation frameworks to support performance testing at scale.
•  Performance Analysis: Analyze model and service latency, throughput, and error rates using observability platforms such as Grafana, Datadog, and Splunk to uncover performance trends and anomalies.
• Bottleneck Identification: Identify performance bottlenecks across Go-based microservices and Kubernetes infrastructure, and recommend targeted optimizations.
• Cross-Functional Collaboration: Partner closely with Engineering, QE, DevOps, and AI/ML teams to drive measurable performance improvements and embed performance best practices.
• Documentation: Create and maintain clear test plans, findings, and technical recommendations for both technical and non-technical stakeholders.
• CI/CD Integration: Help integrate performance testing into CI/CD workflows to enable continuous performance validation.

Minimum Qualifications

• 5-10 years years of experience performance-testing complex distributed systems.
• Advanced proficiency in Python for test automation and tooling.
• Hands-on experience with Locust or similar load-testing tools (e.g., JMeter, Gatling, k6).
• Experience with monitoring and observability platforms (Grafana, Datadog, Splunk, or similar).
• Strong working knowledge of Kubernetes and containerized architectures.
• Deep understanding of performance metrics, system tuning, and sustained-load (soak) testing.
• Ability to translate test data into clear, actionable technical recommendations.
• Familiarity with UI performance testing and CI/CD pipelines.

Preferred Qualifications

• Experience testing AI/ML systems and model endpoints.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with Go- or Python-based distributed services.