Senior Solutions Architect, AI and HPC Infrastructure

Role overview Advise energy-sector organizations on designing, deploying, and improving AI and high-performance computing infrastructure, from data centers to edge environments. The role spans customer discovery and architecture through testing and production guidance, working across technical and commercial teams to make accelerated computing systems fit real energy workloads.

Responsibilities - Lead technical discovery, architecture reviews, deep dives, proofs of concept, benchmarks, and demonstrations with customers, software partners, and account teams. - Guide the design and optimization of GPU clusters, AI infrastructure, and edge deployments for energy applications. - Address infrastructure needs across compute, networking, storage, containers, monitoring, security, power, and data center operations. - Promote telemetry and management practices that improve utilization, reliability, performance, and workload visibility. - Develop reusable architectures, sizing guidance, deployment playbooks, benchmark reports, demos, and technical publications. - Share findings and technical guidance with customers and internal teams, and build relationships with key customers and solution partners.

Requirements - Bachelor’s, master’s, or doctoral degree in computer science, engineering, physics, or a related discipline, or equivalent experience. - At least eight years of hands-on experience in areas such as AI infrastructure, accelerated computing, distributed systems, cloud infrastructure, HPC, or machine learning platforms. - Experience designing, deploying, and operating accelerated infrastructure at scale. - Knowledge of AI cluster orchestration, scheduling, automation, CI/CD, and data center networking such as InfiniBand, Ethernet, or RDMA. - Familiarity with infrastructure for distributed training, inference, model deployment, storage performance, and cluster reliability. - Working knowledge of at least one energy sector, plus strong communication, documentation, problem-solving, and project leadership skills.

Nice to have - Experience with large GPU clusters, AI factories, multi-node training, or production inference platforms. - Experience deploying LLM training, fine-tuning, retrieval-augmented generation, or inference workflows. - Familiarity with MLPerf, HPL, OpenMPI, NCCL, distributed training frameworks, Docker, or Kubernetes.

Benefits and work setup - Primarily collaborative work through virtual tools; customer travel may reach 20%. - Listed base salary ranges are $184,000–$287,500 for one level and $224,000–$356,500 for another. Equity and benefits are also offered; compensation depends on location and experience.