Senior Solutions Architect, AI and HPC Infrastructure

This is a fully remote position, open to applicants in Texas.

📋 Description

• Collaborate with customers in the energy sector, independent software vendors (ISVs), and account teams on discovery sessions, architecture evaluations, technical deep dives, proofs of concept (POCs), benchmarks, demonstrations, and guidance for production deployment.

• Provide expertise on the design, development, and enhancement of accelerated AI infrastructure, which includes large-scale GPU clusters, AI factories, and edge deployments.

• Assist in designing infrastructure that encompasses compute, networking, storage, containers, observability, security, power, and data center operations.

• Promote the use of systems monitoring, telemetry, and management tools to enhance cluster utilization, reliability, performance, and workload insights.

• Record insights and share knowledge through samples, whitepapers, blogs, and by resolving customer issues.

• Create reference architectures, deployment guides, sizing recommendations, benchmark reports, technical playbooks, demonstrations, and whitepapers.

• Form strategic partnerships with key customers and industry-specific solution partners.

• Engage primarily using virtual collaboration tools.

• May require travel up to 20% for customer meetings.

⛳️ Requirements

• Bachelor’s, Master’s, or PhD in Computer Science, Electrical/Computer Engineering, Physics, another Engineering discipline, or a related field (or equivalent experience).

• Over 8 years of practical experience in AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, high-performance computing, or machine learning platforms.

• Significant experience in designing, deploying, and managing accelerated computing infrastructure at scale.

• Comprehensive understanding of AI cluster orchestration, scheduling, automation, and continuous integration/continuous deployment (CI/CD) pipelines.

• Knowledge of data center networking technologies such as InfiniBand, Ethernet, RDMA, network configuration, and performance optimization.

• Familiarity with distributed training, inference serving, model deployment, storage performance, and cluster reliability criteria.

• Practical knowledge of one or more energy sectors along with their operational and IT ecosystems.

• Experience in architecting AI factories, large GPU clusters, multi-node training environments, or production inference platforms.

• Proven experience deploying large-scale AI infrastructure for LLM training, fine-tuning, retrieval-augmented generation (RAG), and inference workflows.

• Background in evaluating cluster performance using MLPerf, HPL, or specific workload performance assessments.

• Applications and systems-level expertise in OpenMPI, NCCL, distributed training frameworks, and GPU communication patterns.

• Proficient with Docker/Containers and Kubernetes.

• Exceptional skills in presentation, communication, problem-solving, documentation, and collaboration.

• Strong ability to structure problems and self-direct efforts to drive complex, multi-stakeholder projects to successful outcomes.

🏝️ Benefits

• Equity

• Comprehensive benefits package