AI Engineer: RAG & On-Prem Systems (Hybrid)

Cph Ai Hub; Aarhus; Hybrid; DKK 650,000 - 900,000; Full time

Benefits offered by this job

Beautiful offices at Viby J? station (

Free parking

Opportunity to work hybrid

Competitive salary

Short decision-making process

Personal development

Job summary

MicroTech Software is building production AI-powered tools for insurance professionals, including RAG chatbots and agentic workflows. You will work with an advanced team to shape the architecture of a live AI product and drive decisions across the stack.

The role focuses on on-prem to local model migration, Danish-language RAG quality considerations, and building scalable, maintainable systems. Hybrid work and strong professional_growth opportunities are offered.

Qualifications

• 2–4 years of production Python engineering experience, including async code.
• Direct experience with LLM orchestration frameworks (LangChain or similar).
• Solid understanding of RAG systems: chunking, embeddings, vector search.
• Familiarity with vector databases (pgvector, Chroma, Weaviate, Pinecone, or equivalent).
• Comfort owning a FastAPI service end-to-end: schema design, error handling, auth, containerization.

Responsibilities

• Shape the architecture of a live AI product.
• Evaluate and challenge design decisions across the stack and propose improvements.
• Plan technically with the team and stakeholders, translating requirements into tasks.
• Identify technical debt and justify addressing it.
• Design and lead the migration to a fully local on-prem LLM stack.

Skills

Python

Async programming

Clean architecture

Testing

LLM orchestration

Tools

LangChain

FastAPI

Vector databases

Job description

MicroTech Software is building production AI-powered tools for insurance professionals, including RAG chatbots and agentic workflows. You will work with an advanced team to shape the architecture of a live AI product and drive decisions across the stack.

The role focuses on on-prem to local model migration, Danish-language RAG quality considerations, and building scalable, maintainable systems. Hybrid work and strong professional_growth opportunities are offered.