AI Field Engineer – (Remote) FW-AI

SIVAD Consulting; United States; Remote; USD 176,000 - 200,000; Full time

Job summary

SIVAD Consulting is seeking an AI Field Engineer (Remote) to embed with enterprise customers and turn GenAI challenges into production systems. You\'ll lead discovery, scope POCs, and deploy production solutions in customer environments.

The role requires 4+ years of hands-on experience with LLM inference/training, Python, cloud ecosystems, and Kubernetes, plus strong buyer-facing communication and executive presence.

Qualifications

• 4+ years of professional AI/ML field experience.
• Hands-on with LLM inference or training, including fine-tuning.
• Proven ability to ship production code inside customer environments.

Responsibilities

• Lead technical discovery calls, scope POCs, and validate model deployments.
• Build end-to-end POCs and production integrations inside customer environments.
• Guide model selection and fine-tuning strategies for production at scale.
• Manage multi-stakeholder enterprise relationships to move deals forward.
• Provide feedback from deployments to product roadmap.

Skills

LLM inference

Python

Cloud infrastructure

Kubernetes

Executive presence

Pre-sales

Customer-facing

Production deployments

Tools

vLLM

SGLang

TensorRT-LLM

AWS

Azure

GCP

Job description

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