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Opis oferty

Internship - Generative AI for Technical System Generation (m/f) w Poznań, Polska

PHOENIX CONTACT BUSINESS SERVICES SPÓŁKA Z OGRANICZONĄ ODPOW...

  • ZdalniePraca z dowolnego miejsca
  • HybrydowoPołączenie biura i pracy zdalnej
  • Pełny etatStandardowy tygodniowy wymiar godzin
  • KontraktUmowa czasowa lub freelancing

Poznań, Polska

Operating system, Windows

Your responsibilities, Develop and improve a retrieval system for technical product data (RAG), Implement evaluation methods for GenAI system performance and output quality, Design and optimize prompts for LLM-based workflows (Prompt Engineering), Build and prototype agentic AI systems for complex engineering tasks, Support the development of Python-based AI pipelines and research prototypes

Bachelor's (final year) or Master's student in Computer Science, AI, Data Science, or a related field, Strong programming skills in Python, Familiarity with Large Language Models (LLMs) and Generative AI concepts, Experience with Git and collaborative software development, Interest in Retrieval-Augmented Generation (RAG), prompt engineering, and AI agents, Independent, structured, and proactive way of working, CV in English

What we offer, 6-month paid internship with the possibility of extension or a permanent contract, Hybrid work environment, Flexible working hours, so you don’t have to compromise on your university commitments, Mentorship from experienced developers to gain professional experience, Hands-on experience with real-world projects that go beyond university coursework, State of the art hardware

Benefits, remote work opportunities, flexible working time, integration events, coffee / tea, Paid Internship

PHOENIX CONTACT BUSINESS SERVICES SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ, We are looking for a motivated Bachelor's (final year) or Master's student to support an industrial research project on using Generative AI for the automated generation of technical systems. The role focuses on software development and the implementation of modern LLM-based methods, with an average workload of at least 20 hours per week.

This is how we work,

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