Principal Solutions Architect, Foundation Model Providers

## Responsibilities - Maintain and foster relationships with model providers, becoming their trusted technical advisor and strategic partner - Develop deep knowledge of core foundational services (Compute, Network, Storage) along with ML expertise to build long-term relationships with customer engineering teams - Dive deep to understand the details of model provider’s environment, business goals, and technical requirements for building and deploying foundation models - Design and implement advanced cloud architectures that enable model providers to scale their AI research and production workloads efficiently - Partner closely with AWS service teams (EC2, Global Networking, EKS, Bedrock, S3) to influence roadmaps and develop custom solutions that meet model provider’s unique requirements - Identify patterns and technical solutions that can be broadly applied across the FMP segment to accelerate innovation - Lead technical discussions that articulate the business value of AWS platform and services to both technical architects and executive stakeholders - Drive technical and architectural best practices for GPU optimization, network throughput, distributed training, and cost efficiency at massive scale ## Requirements - 10+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience - Bachelor's degree in computer science, engineering, mathematics or equivalent - Experience developing technology solutions and evangelising end-to-end technology roadmaps that guide IT transformations toward cloud computing - Experience communicating across technical and non-technical audiences and at C-level, including training, workshops, publications ## Nice to Have - Knowledge of large scale automation and workflow management or equivalent - Knowledge of presentations and whiteboarding skills with a high degree of comfort speaking with internal and external executives, IT management, and developers - Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets - Experience with CUDA kernels or ML/low-level kernels, or experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution - Experience in Kubernetes, Docker or containers ecosystem - Knowledge of foundation model architectures, training approaches, and serving infrastructure ## Benefits - Inclusive Team Culture - Mentorship & Career Growth - Work/Life Balance - Diverse Experiences - Why AWS?