Account Solution Architect - Financial Services

Weights & Biases Weights & Biases · Data AI · New York, NY · Global Field Organization

Account Solutions Architect for Financial Services at CoreWeave, a cloud provider focused on AI workloads. This role involves being a technical partner to existing financial services customers, helping them deepen platform adoption, identify expansion opportunities, and scale their AI workloads in production. The role requires understanding customer needs around model development, deployment, governance, and infrastructure efficiency, and working with sales, product, and engineering teams to ensure customer success.

What you'd actually do

  1. Own the technical relationship with existing financial services customers across CoreWeave’s full platform: infrastructure, Models, Weave, observability, and inference.
  2. 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.
  3. Understand how financial services customers approach model development, research velocity, evaluation, production deployment, governance, performance, reliability, and infrastructure efficiency.
  4. Partner closely with Account Managers on the commercial motion, with Specialist Field Engineers for deep domain expertise, and with customer teams to ensure they are getting maximum value from CoreWeave.
  5. 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.

Skills

Required

  • 4+ years of relevant experience in a solutions engineer, AI-oriented solutions consultant, or technical field engineering role
  • Proficiency 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

Nice to have

  • 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)

What the JD emphasized

  • 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

Other signals

  • customer-facing technical partner
  • deepen platform adoption
  • scale AI workloads in production
  • understanding how financial services customers approach model development, research velocity, evaluation, production deployment, governance, performance, reliability, and infrastructure efficiency