Vishalendu Pandey — CEO

Performance Engineering Leader with 20+ years of experience designing, validating, and scaling large-scale enterprise and SaaS platforms. I operate comfortably as a Principal/Staff-level Individual Contributor as well as in Director-level technical leadership roles. My core focus is performance-driven system design for cloud-native, distributed architectures. I work at the intersection of non-functional requirements, scalability engineering, observability, and production readiness. I have extensive experience defining SLIs/SLOs, modeling realistic workloads, validating resiliency and disaster recovery scenarios, and driving capacity and cost optimization decisions. Technically, I specialize in Java/Spring Boot platforms deployed on Kubernetes (AKS), with deep experience across Kafka, MongoDB Atlas, Snowflake, and distributed telemetry systems. I work extensively with Prometheus, Grafana, OpenTelemetry, and JVM profiling tools to diagnose complex bottlenecks across application, infrastructure, and data layers. Beyond traditional load testing, I build Python-based exploratory data analysis (EDA) pipelines to correlate metrics across observability backends and generate repeatable, insight-driven performance reports using Jupyter. My approach emphasizes measurable signal over synthetic benchmarks. More recently, I have extended performance engineering practices into agentic AI and LLM-backed systems, validating latency, concurrency, failure modes, and cost behavior of backend APIs and frontend workflows. I use tools such as Langfuse to analyze execution paths, token usage, and performance-cost tradeoffs in AI-driven architectures. Over the course of my career, I have influenced architecture from early design through production, supported peak-scale readiness, participated in go/no-go release decisions, and mentored performance engineers to build strong, scalable engineering practices within product organizations. I am particularly interested in performance-driven architecture, distributed systems, applied AI systems, and building engineering cultures where scalability, reliability, and efficiency are first-class design concerns.

Stackforce AI infers this person is a SaaS Performance Engineering Leader with deep expertise in cloud-native architectures.

Experience: 20 yrs 5 mos