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infrastructure, including Docker-based microservices, large language model (LLM) inference servers on GPU clusters, vector database and retrieval-augmented generation (RAG) pipelines, and observability stacks
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microservices, large language model (LLM) inference servers on GPU clusters, vector database and retrieval-augmented generation (RAG) pipelines, and observability stacks that advance AI capabilities across
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database system Familiarity with geospatial data types and tools (vector/raster concepts, GDAL, spatial indexing basics) Preferred Qualifications: Experience with Parquet and GeoParquet, including hive
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application programming models such as CUDA, HIP, SYCL, OpenMP, Kokkos or Raja, vectorization; MPI; and one-sided asynchronous programming models. Experience in heterogeneous computing, developing and debugging
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research user workflows. Preferred: experience with geospatial data workflows, including large geospatial/raster/vector datasets, spatial ETL pipelines, and performance considerations for geospatial
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implementation for core capabilities such as distributed training and inference, workflow orchestration, GPU/accelerator utilization, model registries and artifact management, vector search and retrieval-augmented
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evaluate scalable AI infrastructure architectures for critical infrastructure applications, including data ingestion, vector search, model serving, workflow orchestration, monitoring, and deployment across
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Understanding of multidimensional and tabular modelling, vector databases, Graph DB Experience using Microsoft Visual Studio or Visual Studio Code, Python, PyTorch, TensorFlow Two to three years of experience
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for raster and vector data (projection harmonization, spatial joins, tiling/chunking, and QA/QC). Develop geospatial validation frameworks for model outputs (e.g., comparisons to reference datasets, spatial
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clearance, DoD TS with SCI and Poly Understanding of multidimensional and tabular modelling, vector databases, Graph DB. Experience using Microsoft Visual Studio or Visual Studio Code, Python, PyTorch