LLM Applications Engineer

Location: New York | San Francisco | Munich | London (In-Person)
Employment Type: Full-Time
Base Salary: $130,000 – $175,000

Overview

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.

What Youll Do

• 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

What Were Looking For

Core Requirements

• 2+ years of full-stack web development experience

• Proficiency in Python and JavaScript or TypeScript

• Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, 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

Strong Plus

• 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

Who This Is Not For

This role is not a fit if you:

• 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

What Success Looks Like

• 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

Why This Role Is Unique

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.