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Liquid AI

Solutions Architect

Liquid AI

Solutions Architect bridging AI capabilities with customer needs at Liquid AI. Collaborating with clients to deliver customer engagement solutions leveraging edge AI technologies.

Posted 4/14/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies
C++Cloud

About the role

Key responsibilities & impact
  • 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 (including LEAP for fine-tuning, domain adaptation, and evaluation) to drive technical wins
  • Lead technical discovery: map current-state customer architectures to Liquid solutions, drive competitive positioning against open-source and incumbent models, and quantify ROI for both cost-optimization and new-experience use cases
  • Co-own the product-field feedback loop: document friction patterns, eval failures, and capability gaps from engagements and partner with product and research to influence roadmap
  • Turn engagement learnings into reusable assets: reference architectures, solution primitives, demo building blocks, engagement playbooks, and vertical-specific solution patterns across Liquid's priority industries

Requirements

What you’ll need
  • Applied ML skills: hands-on experience working with ML models in customer-facing contexts (building demos, prototypes, or production integrations)
  • Pre-sales and post-sales experience: you have owned technical customer engagements end-to-end, not just the pitch
  • Strong customer-facing communication: you can run discovery, build relationships with technical and business buyers, and present to executives
  • Understanding of AI architectures and deployment tradeoffs: token efficiency, on-device vs. cloud, model size vs. latency, open-weight vs. proprietary
  • Familiarity with small or efficient model deployment (edge, on-device, latency-constrained environments)
  • Track record of creating thought leadership content, technical blogs, or presenting at industry events
  • Familiarity with efficient model deployment: quantization (INT4/INT8, GGUF, AWQ), model serving frameworks (vLLM, TensorRT-LLM, llama.cpp), and hardware-aware optimization for edge or latency-constrained environments
  • Experience designing and debugging model evaluations—you understand why benchmark results can diverge from production performance and know how to diagnose the root cause

Benefits

Comp & perks
  • 100% of medical, dental, and vision premiums for employees and dependents
  • 401(k) matching up to 4% of base pay
  • Unlimited PTO plus company-wide Refill Days throughout the year

ATS Keywords

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Hard Skills & Tools
machine learningmodel evaluationmodel deploymentquantizationmodel serving frameworksedge deploymentcustomer engagementtechnical validationROI quantificationsolution architecture
Soft Skills
customer-facing communicationrelationship buildingdiscovery facilitationexecutive presentationthought leadershipcontent creationcollaborationproblem-solvinginfluencingdocumentation