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Internship - Retrieval-Augmented Generation Architectures (m/w/x)
Designing and implementing RAG components in Python for data value creation. Experience with large datasets and cloud platforms required. Mentoring by senior consultants, access to D ONE Academy.
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Anforderungen
- Current enrollment in Bachelor's, Master's, or PhD in Computer Science, Data Science, Computational Linguistics, Engineering, Mathematics, or comparable field
- Interest in at least one industry domain and curiosity about data value creation
- Down-to-earth, pragmatic, results-oriented attitude
- Experience with large data sets and databases
- Knowledge in relevant technologies and programming languages (e.g. Python, SQL, Git, Spark, cloud platforms)
- Excellent communication skills in English
- Working understanding of large language models, embeddings, and vector search
- Hands-on experience with orchestration libraries (e.g. LangChain, LlamaIndex, Haystack)
- Hands-on experience with vector stores (e.g. pgvector, Qdrant, Weaviate, Chroma, FAISS)
- Familiarity with document ingestion, chunking, and indexing strategies
- Familiarity with hybrid retrieval and re-ranking
- Software engineering discipline: modular package design, configuration management, dependency management, unit testing, CI
- Ability to run models locally (e.g. Ollama, vLLM, llama.cpp) and via hosted APIs
- Ability to containerize workloads with Docker
- Exposure to at least one cloud platform (Azure, AWS or GCP) and its managed AI and vector services
- Comfort with structured experimentation: parameter sweeps, experiment tracking, reproducible runs
- An eye for performance and cost: latency, throughput, token economics, caching
- Strive to understand client needs
- Deliver outstanding results with high business value in comprehensible form
- Intellectual agility for easy-to-do-business-with approach
- Value clever and creative team play
- Work independently and structure own progress
- Know when to pull in a senior colleague
Aufgaben
- Review state of the art in RAG
- Select approaches worth building on
- Design and implement RAG components in clean, modular, well-tested Python
- Run solutions across local and cloud environments
- Reason about trade-offs
- Benchmark configurations systematically
- Interpret benchmarking results
- Document your work
- Present work to technical and non-technical audiences
Ausbildung
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Tools & Technologien
Benefits
- Low noise environment
- International team
- Freedom to shape approach
- Mentoring by senior consultants
- Access to D ONE Academy
- Expert groups
- Knowledge sharing sessions
- Working groups
- Clearly scoped topic ownership
- Frequent offsite social events
- Path to consultant role
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Zur Originalanzeige bei D ONE Value Creation AGÜber das Unternehmen
The company believes every company will be a data company and helps clients make sense of their data to generate value.
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