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Open nowFirst seen 4 days ago

AI Field Engineer – (Remote) FW-AI

Sivad Consulting50 open roles

Where
Remote
Work mode
Remote
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Your applicationOpen nowAI Field Engineer – (Remote) FW-AISivad Consulting · Remote

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This job: first seen 4 days ago

The posting

AI Field Engineer – (Remote) FW-AI

$176k - $200k

We are looking for an AI Field Engineer (Enterprise) with 3+ years of experience to embed with Fireworks AI's most ambitious enterprise customers and turn complex GenAI challenges into production systems — fast. You'll be the technical tip of the spear, pairing deep hands-on engineering with the executive presence to earn trust across large organizations and drive deals from first discovery call to production deployment.

Responsibilities

  • Lead technical discovery calls, scope POCs, and run load tests and evaluations to validate the right model architecture and deployment configuration for each enterprise customer
  • Build end-to-end POCs and production integrations hands-on-keyboard inside customer environments, navigating their infrastructure, security requirements, and organizational constraints
  • Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation frameworks — moving them from open-model exploration to production at scale
  • Manage multi-stakeholder enterprise relationships — identifying technical champions, navigating org politics, and aligning the right people to move deals forward quickly
  • Feed recurring customer pain points and deployment patterns back into the product roadmap, acting as a direct feedback loop between the field and engineering

Key Requirements

  • 4+ years of experience
  • Deep hands-on experience with LLM inference and/or training — working knowledge of open-model frameworks (vLLM, SGLang, TensorRT-LLM) and fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus); candidates with only closed-model/API-wrapper experience will not clear the bar
  • Proven ability to ship production code inside a customer's environment — not just advisory work; you've built and deployed POCs/MVPs that ran in someone else's prod system
  • Strong Python skills plus GPU/cloud infrastructure experience (AWS, Azure, or GCP) and comfort with Kubernetes
  • Executive presence and enterprise navigation skills — able to run a technical deep-dive with an ML engineer and present architecture trade-offs to a VP in the same afternoon
  • Pre-sales or customer-facing field engineering experience (FDE, Applied AI Engineer, Solutions Architect, or similar); pure software engineers without customer-facing exposure are not a fit
  • GenAI infrastructure management — deploying and operating open-​model inference
  • Hyperscaler experience in an AI capacity
  • In client-facing AI/ML roles — solutions architects, forward-deployed engineers, customer success engineers, Applied AI Engineers, Sales Engineers, AI Infra/ML Engineers, and Software Engineers with client-facing exposure
  • Domestic travel to enterprise customers as needed

Tech stack

  • Python, vLLM, SGLang, TensorRT-LLM, Kubernetes, AWS, Azure, GCP, Azure AI Foundry, AWS Bedrock, AWS SageMaker, GCP Vertex AI, LLM Fine-Tuning (SFT, DPO, RFT), GPU Infrastructure, Open-source LLM frameworks
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