Note: The job is a remote job and is open to candidates in USA. 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
Skills
- 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
Benefits
- Hybrid (US-based, remote-friendly) work mode
- Domestic travel to enterprise customers as needed
Company Overview