Natural Language Processing Engineer

Principal NLP Scientist

• Location: Amsterdam or Munich
• Employment Type: Full-time

About our client

Our client is a premier global distributor of electronic components. They are rapidly expanding and heavily investing in next-generation AI and machine learning to revolutionize procurement automation worldwide.

Job Summary

We are seeking a Principal NLP Scientist to serve as a senior technical leader and individual contributor directly reporting to the CTO. In this role, you will be responsible for designing, researching, and improving advanced Natural Language Processing (NLP) and Large Language Model (LLM) capabilities for our core production business systems. You will bridge the gap between applied research and practical product impact—transforming state-of-the-art NLP technologies into reliable, measurable, maintainable, and highly efficient production workflows that power the future of electronic supply chains.

Responsibilities

• Technical Leadership & Strategy: Define the scientific and technical direction for NLP and LLM capabilities across our internal platforms and operational workflows. Provide expert guidance on what is technically viable, reliable, and production-ready.
• Model Development & Applied Research: Design, evaluate, and scale NLP solutions for named entity recognition (NER), text classification, text generation, semantic search, document understanding, and information extraction.
• LLM & RAG Integration: Lead the design and deployment of Large Language Models, hybrid retrieval-augmented generation (RAG) pipelines, semantic search, and GraphRAG architectures.
• Cross-Functional Collaboration: Partner closely with software engineers, data engineers, product managers, data analysts, and data annotation teams to build, evaluate, and constantly refine language-driven automation systems.
• Experimentation & Quality: Design rigorous, measurable experiments, establish evaluation baselines, define success metrics, compare model approaches, and clearly communicate the trade-offs to both technical and non-technical stakeholders.
• Data Strategy: Lead standards for training data preparation, annotation strategy, labeling guidelines, and model validation quality.
• Mentorship: Provide technical leadership and guidance to help mentor and grow junior machine learning and NLP team members.

Required Qualifications (Must-Haves)

• Education: PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics, Applied Mathematics, Data Science, or a closely related technical field.
• Experience:
  • 8+ years of professional experience in machine learning, artificial intelligence, or NLP.
  • 5+ years of hands‑on experience building, deploying, and maintaining NLP models for production or near-production systems.
• Deep Neural Architectures: Strong understanding of modern neural network architectures, including RNN, CNN, Transformer-based architectures, attention mechanisms, embeddings, fine-tuning strategies, layers, modules, and loss functions.
• Core NLP Tasks: Practical experience with NER, text classification, text generation, semantic similarity, and document understanding.
• LLM & RAG Expertise: Deep experience working with Large Language Models, including model evaluation, prompt design, fine-tuning, retrieval-augmented generation, and deployment for safe production usage.
• Technical Stack:
  • Strong hands‑on coding skills in Python.
  • Strong experience with PyTorch and Hugging Face Transformers.
  • Experience with ONNX or other model optimization / model serving formats.
  • Proficiency with standard data libraries: Pandas, NumPy, SciPy, scikit-learn, and Matplotlib.
  • Experience working with SQL databases and querying structured business data.
  • Cloud & Tools: Hands‑on experience with cloud platforms (Microsoft Azure or AWS) and experience working within Agile software engineering workflows.
  • Leadership & Communication: Ability to provide technical leadership and mentor junior engineers without requiring formal people management authority. Excellent written and verbal English communication skills.

Preferred Qualifications (Nice-to-Haves)

• Experience leading NLP or AI research initiatives within a commercial, production-focused environment.
• Experience with multilingual NLP systems.
• Familiarity with vector databases, semantic search architectures, and knowledge graph concepts (including LLM integration with graph databases like Neo4j, FalkorDB, or similar).
• Knowledge of model serving, monitoring, drift detection, and production ML observability tools.
• Experience with Docker, containerized ML workloads, and modern MLOps / CI/CD pipelines.
• Experience working directly with data annotation teams to draft high-quality labeling instructions.
• Experience with .NET / C#, ASP.NET Core, or integrating ML services into enterprise software platforms.
• Academic publications, patents, or recognized public technical contributions in NLP, ML, or applied AI.