Apply now

Principal AI/RAG Engineer

Intellias Show all offers

Łódź

Kraków

Wrocław

Warszawa

Expires Nov 17, 2026

7 days ago

In short

Intellias seeks a Principal AI/RAG Engineer in Łódź to build AI-ready data platforms for investment management. Responsibilities include designing production-grade AI/RAG solutions, evaluation frameworks, and retrieval pipelines. Requires 6+ years Python, 2+ years LLM/RAG, and expertise in vector databases & hybrid search.

Keywords

Python LLM RAG Vector Databases Hybrid Search Data Engineering Information Extraction PostgreSQL AI Agents

AI-written summary based on the listing content.

About the Project

Our client is a leading global investment management company headquartered in London, managing over $228 billion in assets for institutional investors worldwide. Data science, machine learning and AI play a central role in the firm's investment and research processes.

As part of this engagement, we are building the foundations that enable safe and scalable AI adoption in highly regulated financial environments. The focus is on creating AI-ready data platforms that allow agents to securely discover, understand and reason over enterprise data.

This is an opportunity to work at the intersection of Data Engineering, AI, RAG systems and enterprise-scale information management.

Your Role

We are looking for an experienced Principal AI/RAG Engineer who combines strong Python engineering skills with hands-on expertise in LLMs, Retrieval-Augmented Generation (RAG), and modern data platforms.

You will design and build the catalog, semantic, metadata and analytical layers that transform complex on-premise data estates into AI-accessible systems. You'll play a key role in improving retrieval quality, building evaluation frameworks, developing extraction pipelines and enabling trusted AI-powered experiences.

Responsibilities

Design and develop production-grade AI and RAG solutions.
Build and maintain evaluation frameworks, automated test suites and quality gates for AI systems.
Enhance retrieval pipelines using hybrid search, reranking and metadata-driven filtering.
Develop document intelligence solutions, including structure-aware parsing, chunking and information extraction.
Build metadata and entity extraction pipelines with confidence scoring and human review workflows.
Design synchronization mechanisms for enterprise content platforms.
Develop document lineage, temporal views and document relationship models.
Contribute to knowledge graph and query orchestration capabilities.
Create monitoring, observability and quality dashboards for AI services.
Ensure provenance, traceability and governance across all generated answers and extracted information.
Collaborate directly with client stakeholders in a regulated financial environment.