Solutions Architect

Fireworks AI · Data AI · New York, NY +1 · Engineering

Solutions Architect role focused on customer engagement, technical sales, and solution design for generative AI infrastructure, specifically LLM inference and fine-tuning. The role involves understanding customer needs, designing AI solutions using the Fireworks platform, executing Proofs of Concept (POCs), and providing performance engineering and model recommendations. It requires strong technical depth in the LLM stack and customer-facing skills, with two tracks: Enterprise SA and Applied AI SA.

What you'd actually do

  1. Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions
  2. Design end-to-end architectures for GenAI applications covering model selection, inference configuration, RAG design, and fine-tuning strategy
  3. Define what a minimal, compelling proof-of-concept looks like and own it through to delivery. Prioritize and stack rank opportunities: manage scope creep, set realistic timelines, and keep the customer aligned on what success looks like
  4. Run inference sweeps and establish performance baselines for customer workloads
  5. Guide customers on fine-tuning strategy and model recommendations: when to use SFT, DPO, or RFT, and which model family fits their use case

Skills

Required

  • 5+ years in a technical, customer-facing role (Solutions Architect, Sales Engineer, Forward Deployed Engineer, Customer facing AI Engineer / Data Scientist or equivalent)
  • Hands-on experience with the LLM stack: inference trade-offs, fine-tuning methodologies (SFT, RFT, DPO), and deploying models at scale
  • Strong Python skills: comfortable reading, writing, and debugging production code

Nice to have

  • Experience with Fireworks platform
  • Experience with RAG design
  • Experience with model selection
  • Experience with inference configuration
  • Experience with performance tuning
  • Experience with customer relationship management
  • Experience with technical sales
  • Experience with executive presentations

What the JD emphasized

  • customer-facing technical role
  • inference, fine-tuning, and model architecture
  • customer's definition of success
  • technical execution
  • shipping working solutions quickly
  • performance engineering
  • fine-tuning strategy
  • model recommendations
  • evaluate model quality
  • robust eval pipelines
  • technical relationship
  • customer signal
  • deployment patterns
  • pain points
  • feature gaps
  • 5+ years in a technical, customer-facing role
  • Hands-on experience with the LLM stack
  • inference trade-offs
  • fine-tuning methodologies
  • deploying models at scale
  • Strong Python skills
  • debugging production code

Other signals

  • customer-facing technical role
  • LLM stack experience
  • inference, fine-tuning, model deployment