Research Intern – LLMs

Vor 6 Tagen

Cologne Bonn Region, Deutschland neuland Vollzeit 30.000 € - 60.000 € pro Jahr

Location:

onsite/Hybrid

Duration:

Internship (6 months)

Team:

Applied AI Research

About the Role

We are looking for a motivated 
Research Intern

to join our applied AI team. The intern will work on advancing 
LLM-driven Retrieval-Augmented Generation (RAG)

systems, focusing on fine-tuning pre-trained language models, developing 
hybrid retrieval + generation pipelines

, and innovating new approaches for orchestration and evaluation. This role offers the opportunity to work on 
cutting-edge generative AI problems

in a fast-paced, research-driven environment.

Responsibilities

  • Research and prototype novel methods for 
    hybrid RAG pipelines

(dense + sparse retrieval, agentic workflows).
- Fine-tune and optimize 
pre-trained LLMs

(parameter-efficient tuning, distillation, quantization).
- Build 
evaluation pipelines

for RAG and LLM tasks (faithfulness, factuality, latency, cost).
- Experiment with 
LangChain

and other orchestration frameworks to connect data, tools, and LLM APIs.
- Implement and benchmark 
LLM optimization techniques

(prompt optimization, caching, inference acceleration).
- Deploy solutions on 
Azure AI stack

and integrate with production-ready APIs.
- Contribute to research reports, documentation, and open-source code when possible.

Requirements

  • Strong programming skills in 
    Python

.
- Solid understanding of 
Transformers

and modern 
LLM architectures

.
- Familiarity with 
Azure OpenAI / Hugging Face / other RAG and
LLM APIs

.
- Understanding of 
retrieval methods

(dense embeddings, vector databases, hybrid search).
- Knowledge of 
optimization techniques

for large models (LoRA, quantization, pruning).
- Strong problem-solving mindset, with the ability to run experiments end-to-end.

Preferred Qualifications

  • Masters or PhD student

in Computer Science, Artificial Intelligence, Data Science, or related field.
- Previous research or project experience in 
RAG, LLM fine-tuning, or IR/NLP

.
- Familiarity with 
evaluation benchmarks

for RAG/LLM systems (e.g., RAGAS, MTEB, faithfulness metrics).
- Exposure to 
cloud-based AI workflows

(Azure, AWS, GCP).
- Knowledge of distributed training or inference acceleration (DeepSpeed, Hugging Face Accelerate).

What You'll Gain

  • Hands-on experience with 
    cutting-edge generative AI research

.
- Opportunity to contribute to 
innovative RAG pipelines

used in real-world applications.
- Mentorship from experienced researchers and engineers.
- Exposure to 
hybrid applied research & product-oriented problem-solving

.

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