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Develop and optimize Elasticsearch performance across distributed and serverless architectures. Build performance models, profiling and benchmarking workflows, regression detection, and AI-assisted optimization harnesses. Analyze bottlenecks in logging, metrics, vector search, and ES|QL; implement scalable, thread-safe, high-performance Java code; and collaborate across teams through technical reviews and guidance. Experience with JVM internals, distributed systems, performance engineering, and benchmarking is required.

Leads architectural and code-level performance engineering for Elasticsearch, from design through production. Develops performance models, profiles distributed systems, identifies bottlenecks, builds benchmarks and regression detection, and drives optimization across logging, metrics, vector search, and ES|QL. Designs AI-assisted optimization harnesses, collaborates across teams, and mentors engineers while improving scalability, predictability, and performance.

Lead architectural and code-level performance engineering for Elasticsearch, from design through production. Develop performance models, profile distributed systems, identify bottlenecks, optimize stateful and serverless architectures, and build benchmarking and regression-detection automation. Drive AI-assisted optimization tooling, collaborate across teams, and mentor engineers. Focus areas include Java and JVM performance, concurrency, distributed systems, logging, metrics, vector search, ES|QL, storage engines, and scalable search infrastructure.

Lead Elasticsearch performance engineering from architecture through production. Develop performance models, optimize distributed systems, profile bottlenecks, build benchmarks, detect regressions, and improve scalability across stateful and serverless architectures. Design AI-assisted optimization and benchmarking tools, collaborate across teams, define performance strategies, and mentor engineers. The role requires deep Java, JVM, concurrency, distributed systems, and search or data-store performance expertise.

Develop and optimize Elasticsearch performance across distributed and serverless architectures. Responsibilities include implementing performance models, profiling systems, identifying bottlenecks, building benchmarks and regression detection, improving logging, metrics, vector search, and ES|QL performance, and creating AI-assisted optimization tools. The role also involves architectural and code-level optimization, technical analysis, peer reviews, and guidance for fellow engineers.

Leads architectural and code-level performance engineering for Elasticsearch, from design through production. Develops performance models, profiles distributed systems, identifies bottlenecks, optimizes logging, metrics, vector search, and ES|QL, and builds benchmarking and regression-detection automation. Drives AI-assisted optimization harnesses, collaborates across engineering teams, establishes performance-focused practices, and mentors engineers. The role requires deep Java, JVM, concurrency, distributed systems, benchmarking, and large-scale data-store expertise.

Own a strategic enterprise territory, generate outbound pipeline, qualify complex opportunities using MEDDPICC, deliver executive presentations and product demos, negotiate high-value multiyear consumption contracts, and maintain accurate forecasts. The role requires technical fluency in cloud economics, observability, security, search, and modern data architectures, while partnering with Solutions Architects, Customer Success, Marketing, and RevOps to drive revenue and customer outcomes.

Leads architectural and code-level performance engineering for Elasticsearch, owning optimization initiatives from design through production. Develops performance models, profiles distributed systems, identifies bottlenecks, builds benchmarks and regression detection, and drives automation through AI-assisted optimization harnesses. Collaborates across teams, mentors engineers, and improves scalability and predictability across stateful and serverless architectures.

Lead architectural and code-level performance engineering for Elasticsearch, from design through production. Develop performance models, optimize distributed systems, profile bottlenecks, build benchmarks and regression detection, and improve stateful and serverless architectures. Create AI-assisted optimization and benchmarking tools, collaborate across teams, and mentor engineers. The role requires deep expertise in Java, JVM memory management, concurrency, distributed systems, profiling, and large-scale data stores, with native library integration and vector search experience as bonuses.

Lead architectural and code-level performance engineering for Elasticsearch, from design through production. Develop performance models, profile distributed systems, identify bottlenecks, optimize logging, metrics, vector search, and ES|QL, and build benchmarks and regression detection for stateful and serverless architectures. Drive AI-assisted optimization automation, collaborate across teams, and mentor engineers. The role emphasizes Java and JVM internals, concurrency, thread-safe and lock-free programming, distributed systems, and large-scale data-store performance.