Sr Performance Engineer

As a Sr. Performance Engineer, you will become part of an experienced engineering team which is building a large-scale UEM platform as a service. You and your team will be responsible for identifying and delivering solutions to performance, scalability, efficiency, and capacity bottlenecks within UEM platform/services. Within your first 30 days of joining our team, you will not only acquire a fundamental understanding of the product's architecture and primary operational procedures, but also a strong understanding of performance baselines, workload patterns, and performance testing and monitoring tools used within the product.

Utilize your knowledge in .NET Framework and .NET Core web services for performance analysis, profiling, and optimization of production systems.

Work with cross-functional teams in the organization to define performance benchmarks, KPIs, and SLOs, and drive continuous performance improvements against them.

Identify, analyze, and eliminate performance bottlenecks across application, infrastructure, and database layers.

Design and execute performance, load, stress, and endurance tests to validate system behavior under varying workloads.

Respond to performance-related incidents and escalations and help prioritize engineering work, from large initiatives to sprint tasks.

Build automation tools that enable performance testing, benchmarking, capacity planning, and continuous performance validation.

Strong expertise in Application Development and software engineering, with hands-on experience in C#, Java, Go, Groovy, or Python, with a focus on performance profiling and optimization.

Hands-on experience with cloud and infrastructure technologies: AWS, vSphere, Google Cloud Platform; deep understanding of network protocols, Linux/Unix system internals, and system performance tuning; CI/CD tools such as GitHub Actions and Artifactory; Infrastructure as Code and configuration management tools like Terraform, Helm, Chef, and Salt; infrastructure domains such as Network, Database, Server, and Storage; operating systems (Linux and Windows).

Experience with performance monitoring, observability, and profiling tools to analyze system behavior and identify bottlenecks: Dynatrace, Grafana, Prometheus, Wavefront, Kibana, Logz.io, dotTrace/dotMemory, SentryOne.

Strong experience in performance engineering practices including load testing, workload modeling, profiling, benchmarking, and capacity planning.

Experience with AI/ML tools such as PyTorch, TensorFlow, Z-Scoring techniques, and LLMs for advanced analysis and anomaly detection is a plus.

Willingness to support performance validation during releases and assist in critical performance incident scenarios when needed.