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 transparent, 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.

📌 Performance Testing Engineer -AI (Bengaluru)
🏢 Xoriant
📍 Bengaluru