Solutions Architect

Liquid AI
San Francisco, United States, Boston, Cambridge
Workplace: HybridFull timeFunction: Solutions Engineering & Sales EngineeringSkills: ["Communication","Presentation","Problem-solving","Customer-facing","Collaboration"]

We are building a solutions architecture function from scratch. As one of the first Solutions Architects, you will own customer engagements end-to-end—from technical validation to go-live—building demos, mapping customer architectures to Liquid solutions, and creating scalable assets and playbooks to accelerate enterprise adoption of efficient AI models.

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FursaFursa
Liquid AI
Liquid AI
5 months ago

Solutions Architect

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 7 hours agoStatus: Live

Job Summary

We are building a solutions architecture function from scratch. As one of the first Solutions Architects, you will own customer engagements end-to-end—from technical validation to go-live—building demos, mapping customer architectures to Liquid solutions, and creating scalable assets and playbooks to accelerate enterprise adoption of efficient AI models.
Location: San Francisco, United States, Boston, Cambridge
Workplace: Hybrid
Employment Type: Full time
Job Function: Solutions Engineering & Sales Engineering

Key Responsibilities

  • •Own customer engagements end-to-end: from qualified opportunity through technical validation, go-live, and ongoing delivery across all customer segments.
  • •Build customer-specific demos and proofs-of-concept using Liquid models to drive technical wins.
  • •Lead technical discovery: map current-state customer architectures to Liquid solutions, drive competitive positioning, and quantify ROI.
  • •Co-own the product-field feedback loop: document friction patterns and influence roadmap with product/research teams.
  • •Turn engagement learnings into reusable assets: reference architectures, solution primitives, demo blocks, and engagement playbooks.

Key Requirements

  • •Applied ML skills with hands-on experience building demos, prototypes, or production integrations for customer-facing contexts.
  • •Pre-sales and post-sales experience: owned technical customer engagements end-to-end.
  • •Strong customer-facing communication: able to run discovery and present to executives.
  • •Understanding of AI architectures and deployment tradeoffs: token efficiency, on-device vs. cloud, model size vs. latency.
  • •Ability to download a model, build a demo, and present it in a boardroom or Jupyter notebook.
Experience:AIEnterprise softwareEdge computing
Skills:CommunicationPresentationProblem-solvingCustomer-facingCollaboration
Tech Stack:JupyterML modelsLEAPDomain adaptationVLLMTensorRT-LLMLlama.cppQuantizationINT4INT8GGUFAWQEdgeOn-device

Company Brief

Liquid AI
Builds AI infrastructure and tooling to enable real-time, distributed machine learning and orchestration across edge and cloud environments, simplifying deployment and management of intelligent applications.
Industry: AI & Machine Learning
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