AI Engineer-Enterprise

Job Description:

Were partnering with a high growth late stage AI infrastructure company to hire multiple AI Field Engineers (Enterprise) who can sit at the intersection of deep generative AI engineering and complex enterprise customer work. This is a customer facing hands on role where youll turn ambitious GenAI ideas into production systems for some of the worlds most sophisticated organizations.

Why this role is compelling

• Late stage AI infra company with recent major funding and strong conviction from top tier investors; well capitalized and scaling quickly.
• OTE in the 220K 280K range with meaningful equity in a 200 person business where ownership can still move the needle.
• Urgent hiring need with a highly engaged hiring team targeting multiple hires in this function over the near term.

What youll be doing

• Lead technical discovery with enterprise customers scope POCs and run load tests/evaluations to validate the right model architectures and deployment setups.
• Build end to end POCs and production integrations directly inside customer environments working through infra security and compliance constraints to get systems live.
• Advise customers on model selection and fine tuning strategies (e.g. SFT DPO RFT) and design evaluation frameworks that get them from experimentation to production at scale.
• Own the technical relationship across complex accounts - identify champions handle detractors and align stakeholders to keep deals and deployments moving.
• Feed recurring patterns and customer pain points back into the product and engineering org as a direct loop from field to roadmap.

Qualifications

What youve done

• 5 years in customer-facing AI/ML or infrastructure roles (Field Engineer Applied AI Engineer Solutions Architect ML Engineer or similar) with a track record of owning technical workstreams in enterprise accounts.
• Shipped real AI/ML production code into customer environments - not just slide decks or advisory engagements.
• Hands-on experience with LLM inference and/or training using open-model frameworks (for example modern serving stacks and fine-tuning workflows such as SFT; exposure to more advanced approaches like DPO or RFT is a strong plus).
• Strong Python plus comfort with GPUs and cloud infrastructure (AWS Azure or GCP) and container/orchestration tools such as Kubernetes.
• Demonstrated executive-level presence: you can dive deep with an engineer and explain trade-offs to senior leadership in the same day.

What theyre not looking for

• Profiles whose LLM experience is limited to closed-model APIs and wrapper libraries without real exposure to open-model inference or fine-tuning.
• Purely advisory or research-only backgrounds without evidence of shipping production systems.
• Pure Big Tech careers with little to no startup field or high-velocity customer-facing experience.