ABOUT THE PROJECT

LLMAPI.ai is building infrastructure for companies that ship AI products in production. Instead of integrating directly with multiple LLM providers, teams use one unified API to access 400+ models, manage costs, monitor usage, optimize routing, and scale reliably.

REQUIREMENTS

– 2–4 years of experience in Solutions Engineering, Sales Engineering, Developer Relations, Technical Support, or Backend Engineering
– Understanding of APIs, SDKs, authentication, cloud infrastructure, and how modern AI products work
– Interest in AI/LLMs and curiosity about the ecosystem
– Ability to explain technical things clearly without overcomplicating them
– Strong communication and soft skills — you’ll spend a lot of time with customers
– Comfortable in fast-moving startup environments with a lot of ambiguity
– Upper-Intermediate+ English
– Nice to have: Experience working with OpenAI, Anthropic, Gemini, or other LLM providers
– Experience with Python, Node.js, or API integrations
– Familiarity with prompt engineering, AI agents, or model evaluation
– Previous experience working alongside sales teams or customer-facing engineers

RESPONSIBILITIES

– Join sales calls as the technical counterpart and help move deals forward
– Understand customer infrastructure, AI workflows, and technical pain points
– Help clients integrate and use LLMAPI.ai successfully
– Explain concepts like model routing, provider fallback, token usage, latency, and API architecture in a simple and practical way
– Support technical demos, proofs of concept, and onboarding
– Work closely with engineering and product teams to communicate customer feedback and edge cases
– Help clients optimize costs, performance, and reliability of their AI stack
– Troubleshoot technical issues and unblock integrations when needed
– Build trust with both technical and non-technical stakeholders

WHAT WE OFFER

– Competitive compensation
– Direct access to founders and fast decision-making
– Work on a real AI infrastructure product used in production
– High ownership and a lot of autonomy
– Fast growth, complex technical challenges, and room to shape the role
– Flexible format and remote-friendly environment

COMPENSATION & BENEFITS

– Competitive compensation
– Direct access to founders and fast decision-making
– Work on a real AI infrastructure product used in production
– High ownership and a lot of autonomy
– Fast growth, complex technical challenges, and room to shape the role
– Flexible format and remote-friendly environment