AI Field Engineer - Enterprise

AI Talent Hunt Cloud is seeking an AI Field Engineer specializing in enterprise solutions. The role involves customer-facing responsibilities in AI/ML field engineering, requiring hands-on experience with LLM inference and production code deployment within client environments.

Responsibilities:

• 3+ years of experience in customer-facing AI/ML field engineering (FDE, Applied AI, Solutions Architect, AI Infra, ML Engineer, Software Engineer with pre-sales exposure, or research backgrounds transitioning to customer-facing roles)
• Shipped AI/ML production code inside a customer's environment
• Hands-on LLM inference and fine-tuning experience — ran SFT pipelines, benchmarked latency, and tuned open-model deployments
• Ran the full field cycle in a pre-sales or customer-facing capacity — discovery, POC scoping, load tests, evals, and model selection
• Background at an AI-native/AI-infra startup (inference, MLOps, developer tooling) or enterprise SaaS with built-in AI features
• LLM serving frameworks (vLLM, SGLang, TensorRT-LLM), agents, inference trade-offs, terminal-comfortable
• Python and Kubernetes proficiency
• Trained open models and familiar with fine-tuning methodologies (SFT, DPO, RFT)
• GPU optimization for LLM workloads
• Demonstrated executive presence in enterprise customer-facing roles
• Navigated enterprise org politics end-to-end — champions, detractors, security reviews, and procurement cycles
• Domestic travel to enterprise customers as needed

Requirements:

• 3+ years of experience in customer-facing AI/ML field engineering (FDE, Applied AI, Solutions Architect, AI Infra, ML Engineer, Software Engineer with pre-sales exposure, or research backgrounds transitioning to customer-facing roles)
• Shipped AI/ML production code inside a customer's environment
• Hands-on LLM inference and fine-tuning experience — ran SFT pipelines, benchmarked latency, and tuned open-model deployments
• Ran the full field cycle in a pre-sales or customer-facing capacity — discovery, POC scoping, load tests, evals, and model selection
• Background at an AI-native/AI-infra startup (inference, MLOps, developer tooling) or enterprise SaaS with built-in AI features
• LLM serving frameworks (vLLM, SGLang, TensorRT-LLM), agents, inference trade-offs, terminal-comfortable
• Python and Kubernetes proficiency
• Trained open models and familiar with fine-tuning methodologies (SFT, DPO, RFT)
• GPU optimization for LLM workloads
• Demonstrated executive presence in enterprise customer-facing roles
• Navigated enterprise org politics end-to-end — champions, detractors, security reviews, and procurement cycles
• Domestic travel to enterprise customers as needed