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Niche HPC, RDMA and SIMD expertise reduces candidate density despite Bangalore location.
Highly specialized systems, database internals, and hardware-aware engineering make cross-industry transfer difficult.
Multiple mandatory low-level systems skills and explicit 6+ years requirement enforce strict filtering.
Lead architecture and development of a next-generation massively parallel processing (MPP) distributed database engine optimized for low-latency and high-throughput use at petabyte-scale data.
Design and implement core database subsystems including query execution engines, distributed transaction managers, SIMD vectorization, and cluster communication protocols (RDMA, RoCE).
Build in-database machine learning inference frameworks and advanced vector/embedding search infrastructures for real-time analytics across a multi-node HPC cluster.
Bachelor's degree in Computer Science Engineering (minimum 4 years) from a reputed institute; Master's preferred for senior roles in HPC/Big Data.
6+ years professional experience in modern C++ (C++17/20/23) including template metaprogramming, coroutines, and memory allocators.
Expertise in HPC parallel programming with MPI and OpenMP, with strong background in low-level hardware optimization (SIMD, cache locality).
Practical experience with distributed database internals, concurrency (lock-free data structures, atomics), and performance tuning/debugging tools.
Experienced systems engineer bridging high-performance computing and big data distributed systems at scale with strong hardware-aware optimization skills.
Comfortable with both architectural leadership and hands-on development in highly parallel, distributed, latency-sensitive environments.
Background or exposure to in-database machine learning pipelines, vector search algorithms, and complex clustered data infrastructures preferred.