LLM Applications Engineer

Location: New York | San Francisco | Munich | London (In-Person)

Employment Type: Full-Time

Base Salary: $130,000 – $175,000

We are hiring an LLM Applications Engineer to build and deploy production‑grade LLM‑powered systems. This is a hybrid AI infrastructure and product engineering role. You will design and implement RAG pipelines, vector retrieval systems, agentic workflows, and full‑stack LLM‑powered product experiences. The role requires hands‑on ownership across backend Python systems and React‑based frontend applications. This position is for engineers who move beyond prototypes and ship robust, scalable LLM systems into production.

• Design and deploy production‑grade RAG (Retrieval‑Augmented Generation) pipelines
• Build and optimize vector retrieval systems
• Implement agentic LLM workflows and orchestration layers
• Develop full‑stack product experiences powered by LLMs
• Design clean, scalable APIs and asynchronous processing systems
• Connect LLM systems to structured data sources, including SQL databases and data engines
• Collaborate closely with product and engineering teams to ship LLM‑first features

• 2+ years of full‑stack web development experience
• Proficiency in Python and JavaScript or Type Script
• Experience with LLM orchestration frameworks such as Lang Chain, Llama Index, or Haystack
• Hands‑on experience building production RAG pipelines and retrieval systems
• Strong API design and asynchronous processing fundamentals
  
  Ability to operate across backend infrastructure and frontend UI
• Computer Science degree from a top‑tier program

• Built and deployed RAG pipelines in live production environments
• Experience working with scientific, technical, or research datasets
• Strong product mindset with LLM‑first feature development
• Experience integrating LLM systems with SQL databases and broader data infrastructure

• Have only academic or prototype‑level LLM exposure
• Have exclusively backend‑only or frontend‑only experience
• Lack experience with vector databases or retrieval systems
• Have not deployed LLM systems into production environments

• Production‑grade RAG pipelines running reliably at scale
  
  Clean, well‑architected retrieval and orchestration systems
• Seamless integration between LLM backends and frontend product experiences
• Measurable product impact from LLM‑powered features
• Ownership of end‑to‑end LLM application architecture

This is not a research‑only AI role and not a traditional full‑stack position. You will sit at the intersection of AI infrastructure and product, building systems that combine retrieval, orchestration, and real‑world user interfaces. If you want to ship meaningful LLM‑powered products — not just experiment with models — this is an opportunity to own that architecture end to end.