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This 6‑month final‑year internship offers a rare opportunity to work on state-of-the-art metadata enrichment, semantic search, embeddings, and RAG (Retrieval-Augmented Generation) inside Amgen’s production ecosystem. You will translate academic techniques into practical, reproducible prototypes using Amgen’s high-quality datasets and collaborate closely with analytics teams, engineers, and domain SMEs. This assignment is ideal for students wanting to bridge academic research with industry‑ready implementation.
Job Responsibility:
Conduct a literature review on modern metadata enrichment, semantic search, and RAG workflows
Compare rule-based, Transformer/LLM‑based, and hybrid extractors + retrievers
Build hypotheses and design evaluation frameworks
Implement baseline extractors
Build LLM prompt pipelines, embedding-based retrievers, and hybrid workflows
Integrate a RAG‑enabled helper tool/playbook
Develop a metadata extractor + embedding + retrieval pipeline using Amgen datasets
Build an interactive dashboard showing catalog completeness, data quality, pipeline freshness, KPI health
Demonstrate how improved metadata + IR methods drive measurable productivity gains
Requirements:
Basic Python (scripts, pandas, small experiments)
SQL fundamentals (SELECT, JOIN, GROUP BY, window functions)
Experience working in notebooks (Jupyter/Colab)
Problem-solving mindset and ability to debug
Clear written & verbal communication
Nice to have:
Exposure to ML/NLP concepts (classification, embeddings, transformers)
Experience with git / basic version control
Any prior work with vector search or embeddings
Experience with Databricks, SQL, Power BI (preferred)
What we offer:
Structured mentorship (1–2 hrs/week)
SME access for annotation & evaluation
Compute resources + production environment
Stakeholder exposure and presentation opportunities
A diverse, international, collaborative environment
Hybrid working flexibility
Gym, vitality programs, healthy cafeteria
Possibility to apply for temporary or permanent roles