Key Responsibilities
HPC & GPU Architecture
Design and implement GPU-accelerated architectures for log ingestion, parsing, indexing, and analytics.
Optimize workloads using GPU technologies (CUDA, RAPIDS, TensorRT) for high-throughput data processing.
Architect distributed HPC environments for large-scale log analytics across on-prem and cloud platforms.
Evaluate and integrate high-performance storage (NVMe, parallel file systems) for log-heavy workloads.
Log Analytics & Observability
Lead architecture and optimization of Splunk environments (indexing, search heads, clustering, data pipelines).
Integrate Splunk with GPU-accelerated data processing frameworks to improve performance and reduce latency.
Design solutions across modern observability tools such as the Elastic Stack (Elasticsearch, Logstash, Kibana), Fluentd, and OpenTelemetry.
Build advanced dashboards, alerting systems, and real-time analytics pipelines.
Security & AI-Driven Analytics
Apply GPU acceleration to security analytics use cases (SIEM, anomaly detection, threat hunting).
Design pipelines for analyzing logs using machine learning and AI frameworks.
Enable use cases such as behavioral analytics, predictive alerting, and automated incident response.
Performance Optimization
Tune Splunk and related platforms for high-ingest, low-latency search performance.
Benchmark GPU vs CPU workloads and drive architectural decisions based on performance gains.
Identify and eliminate bottlenecks across compute, storage, and network layers.
Stakeholder Collaboration
Partner with SecOps, DevOps, SRE, and platform engineering teams to define logging and analytics strategies.
Translate business and operational requirements into scalable HPC-based solutions.
Provide technical leadership in architecture reviews, POCs, and production rollouts.
Required Qualifications
7 10+ years of experience in HPC, Solutions Architecture, or Large-Scale Data Platforms
Deep expertise in GPU computing, including:
CUDA and GPU performance tuning
RAPIDS or similar GPU data processing frameworks
Strong hands-on experience with Splunk Enterprise / Splunk Cloud:
Indexing, search optimization, clustering, forwarders, and data pipelines
Experience with log analytics and observability platforms such as Elastic Stack, Fluentd, or similar
Strong understanding of distributed systems and parallel computing architectures
Experience with Linux systems, scripting (Python, Bash), and automation
Preferred Qualifications
Experience with AI/ML frameworks applied to log analytics (PyTorch, TensorFlow, or RAPIDS cuML)
Familiarity with streaming technologies (Kafka, Spark Streaming, Flink)
Experience with Kubernetes and containerized HPC workloads
Knowledge of security analytics / SIEM use cases
Certifications such as Splunk Architect, NVIDIA certifications, or cloud architect certifications (AWS, Azure, Google Cloud Platform)
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