Staff AI Engineer (LLM)
Confidential
Posted: February 3, 2026
Interested in this position?
Create a free account to apply with AI-powered matching
Quick Summary
Staff AI Engineer (LLM) in Poland
Required Skills
Job Description
Location: Poland
Contract: B2B
Salary: 33.000 - 38.000 PLN net B2B
How this role works:
You will start by building the system as part of our team, working in a focused, greenfield setup. After a few months of introduction to the project, you will seamlessly continue working on the same product directly with the client, becoming part of the client’s team responsible for its long-term development and scaling. There is no handover phase and no context switching - you stay with the same codebase, product, and technical challenges, while gaining long-term ownership and impact.
What you’ll work on:
• Designing, implementing, and deploying end-to-end NLP and deep learning systems
• Building LLM-powered applications that interact with real users
• Developing and maintaining production Python services
• Exposing models and pipelines via REST APIs (FastAPI, Flask)
• Working on retrieval models and techniques (RAG, embeddings, ranking)
• Evaluating, monitoring, and continuously improving model and system quality
• Scaling systems to handle enormous volumes of requests
Biggest challenges in this role:
• Greenfield project built from scratch
• High-scale, user-facing systems with strict performance and reliability requirements
• Designing systems meant for long-term ownership, not short-term delivery
• Balancing model quality, latency, and cost in production LLM systems
What you’ll learn:
• How to build LLM-powered products from scratch and take them to production
• Proven approaches to running LLMs in production at scale
• How to design, evaluate, and evolve NLP systems used by real users
• Best practices for production ML and AI system architecture
What you’ll get to try and experiment with:
• End-to-end ownership of LLM-based systems
• Optimizing retrieval models, RAG pipelines, and inference workflows
• Experimenting with different LLMs, prompting strategies, and system designs
• Solving performance and reliability challenges under heavy traffic