AI Field Engineer – FW-AI

SIVAD Consulting

. Lead technical discovery calls, scope POCs, and run load tests and evaluations .

Posted 10/22/2026full-timeRemote • United StatesMid-LevelSenior💰 $176,000 - $200,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading technical discovery and building production integrations within customer environments, with a strong focus on LLM inference, fine-tuning strategies, and managing enterprise relationships. Proficient in navigating organizational constraints and aligning stakeholders to drive successful AI/ML deployments.

Highest-signal resume keywords

LLM Inference And TrainingPython ProgrammingOpen-Model FrameworksGPU/Cloud Infrastructure ExperiencePre-Sales Or Customer-Facing Field Engineering

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM InferenceFine-Tuning WorkflowProduction Code DeploymentPythonKubernetes

Soft Skills

Executive PresenceEnterprise Navigation

Tools & Technologies

AWSAzureGCPVLLMSGLangTensorRT-LLM

Industry Keywords

GenAI Infrastructure ManagementClient-Facing AI/ML RolesHyperscaler ExperienceTechnical Discovery CallsMulti-Stakeholder Relationships

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesPython

About the role

Key responsibilities & impact

Lead technical discovery calls, scope POCs, and run load tests and evaluations
Build end-to-end POCs and production integrations inside customer environments
Navigate customer infrastructure, security requirements, and organizational constraints
Guide customers on model selection, fine-tuning strategy, and evaluation frameworks
Move customers from open-model exploration to production at scale
Manage multi-stakeholder enterprise relationships
Identify technical champions, navigate organizational politics, and align stakeholders to advance deals
Feed recurring customer pain points and deployment patterns back into the product roadmap
Act as a feedback loop between the field and engineering

Requirements

What you’ll need

4+ years of experience
Deep hands-on experience with LLM inference and/or training
Working knowledge of open-model frameworks: vLLM, SGLang, TensorRT-LLM
Fine-tuning workflow experience: SFT at minimum; DPO/RFT a strong plus
Proven ability to ship production code inside a customer's environment
Strong Python skills
GPU/cloud infrastructure experience with AWS, Azure, or GCP
Comfort with Kubernetes
Executive presence and enterprise navigation skills
Pre-sales or customer-facing field engineering experience (FDE, Applied AI Engineer, Solutions Architect, or similar)
GenAI infrastructure management, including deploying and operating open-model inference
Hyperscaler experience in an AI capacity
Experience in client-facing AI/ML roles
Domestic travel to enterprise customers as needed