Account Solution Architect

Weights & Biases Weights & Biases · Data AI · Toronto, ON · Global Field Organization

Account Solutions Architect for a financial services customer portfolio, focusing on AI/ML and LLM workloads on CoreWeave's cloud platform. Responsibilities include deepening platform adoption, designing end-to-end solutions, guiding customers on AI lifecycle, and resolving technical challenges. Requires Python proficiency, experience with deep learning models, LLM applications, and financial services customer engagement.

What you'd actually do

  1. Serve as the named solutions architect and primary technical point of contact for an existing book of financial services customers across CoreWeave’s full AI platform.
  2. Deepen platform adoption by uncovering new use cases, mapping workloads to CoreWeave capabilities, and designing end-to-end solutions that drive expansion and long-term customer value.
  3. Work hands-on with customer teams to train, fine-tune, evaluate, and deploy deep learning and LLM-based workloads into reliable, production-grade systems.
  4. Guide customers on how to structure their AI lifecycle—model development, research workflows, experimentation, evaluation, deployment, governance, observability, and optimization for performance and cost.
  5. Diagnose and resolve complex, often novel, technical challenges in partnership with enterprise engineering teams, unblocking progress on time-sensitive and business-critical workloads.

Skills

Required

  • Python
  • training deep learning models
  • fine-tuning deep learning models
  • evaluating deep learning models
  • deploying deep learning models
  • LLM architectures
  • designing production LLM-powered applications
  • financial services customers
  • cloud platform experience (AWS, GCP, or Azure)
  • problem-solving
  • communication skills

Nice to have

  • cloud infrastructure for AI workloads
  • GPU compute
  • high-performance networking
  • storage
  • PyTorch
  • VLLM
  • langchain
  • LlamaIndex
  • Slurm
  • Kubernetes
  • hyperparameter optimization
  • experiment tracking tools
  • ML Engineering
  • AI Engineering
  • MLOps
  • LLMOps
  • technical pre-sales
  • solutions architecture
  • NVIDIA H100/H200/B200
  • InfiniBand networking
  • parallel file systems

What the JD emphasized

  • financial services customers
  • AI workloads
  • LLM-based workloads
  • production-grade systems
  • AI lifecycle
  • technical challenges

Other signals

  • customer-facing technical role
  • deep learning and LLM workloads
  • production deployment
  • financial services customers