Account Solution Architect - Financial Services

About the role

The Account Solutions Architect, Engaged, Financial Services is the named technical partner to existing financial services customers across CoreWeave’s full platform: infrastructure, Models, Weave, observability, and inference. You’ll work with some of the most sophisticated AI and compute-intensive organizations in the market, including quantitative trading firms, hedge funds, asset managers, and other financial institutions using AI, ML, and large-scale compute to drive business-critical outcomes.

Responsibilities

• Work with some of the most sophisticated AI and compute-intensive organizations in the market, including quantitative trading firms, hedge funds, asset managers, and other financial institutions using AI, ML, and large-scale compute to drive business-critical outcomes.
• Help these customers solve real-world problems by deepening platform adoption, identifying and driving expansion opportunities, strengthening relationships with key technical stakeholders, and serving as a trusted advisor as they scale AI workloads in production.
• Represent the voice of financial services customers internally, surface product feedback from the field, and proactively address technical blockers and business-critical needs to ensure customer success.

Requirements

• 4+ years of relevant experience in a solutions engineer, AI-oriented solutions consultant, or technical field engineering role.
• ExperienceProficiency in Python
• Hands-on experience training, fine-tuning, evaluating, and deploying deep learning models, including modern LLM architectures.
• Experience designing and deploying production LLM-powered applications for customer use cases.
• Experience working with financial services customers, such as quantitative trading firms, hedge funds, asset managers, banks, or other enterprise finance organizations, with an understanding of their unique technical, operational, and business-critical requirements.
• Familiarity with running AI workloads least one major cloud platform (AWS, GCP, or Azure).
• Demonstrated ability to break down and solve complex, often novel, technical problems with enterprise customers.
• Excellent written and verbal communication and presentation skills, with the ability to translate technical concepts for both engineering and executive audiences.

Preferred

• Working knowledge of cloud infrastructure for AI workloads, including GPU compute, high-performance networking, and storage.
• Familiarity one or more deep learning frameworks (PyTorch) and modern LLM stack (VLLM, langchain / LlamaIndex).
• Experience using Slurm or Kubernetes for ML job orchestration.
• Experience with hyperparameter optimization and experiment tracking tools.
• Background in ML Engineering, AI Engineering, MLOps, or LLMOps.
• Prior experience in a technical pre-sales or solutions architecture role focused on net-new logos or greenfield accounts.
• Familiarity with high-performance GPU infrastructure (e.g., NVIDIA H100/H200/B200, InfiniBand networking, parallel file systems).