ML / NLP Engineer
Role details
Job location
Tech stack
Job description
As a ML / NLP Engineer you will play a key role in driving the development, deployment and optimization of advanced machine learning and natural language processing solutions that directly support the organization's strategic goals. You would be responsible for designing end-to-end ML pipelines, ensuring production readiness and delivering scalable models that generate measurable business impact. Acting as a technical expert, you will provide leadership in adopting MLOps best practices, guiding junior engineers and fostering innovation within the team.
Tasks & Responsibilities
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Develop, optimize, and evaluate new Machine Learning (ML) and Statistical models.
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Design, implement, and deploy scalable ML and Natural Language Processing (NLP) models tailored to specific business needs.
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Select suitable algorithms and fine-tune models to achieve optimal performance.
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Ensure models are production-ready and integrate them seamlessly into existing systems.
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Oversee the entire ML pipeline, from data collection and preprocessing to model training, evaluation, and deployment.
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Implement monitoring and maintenance strategies to ensure the model's performance remains consistent over time.
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Develop data pipelines and preprocess data for training and inference to gain valuable insights into complex datasets, enabling informed decision-making.
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Work closely with data scientists, software engineers, product managers, and other stakeholders to align ML initiatives with business goals.
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Implement evaluation metrics and conduct cross-validation to assess model effectiveness and reliability.
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Present project progress and outcomes to executive leadership., At MDPI, we develop and maintain various platforms in order to better serve the scientific community. Please find here-below a list of our main platforms
Requirements
- Master's degree in Data Science, ML or a related field.
- Over 3 years of experience in ML/NLP Engineering.
- Additional MLOps and AI/Data Science experience is desired.
- Advanced experience with Python for complex applications and ML models.
- In-depth understanding of Artificial Intelligence principle.
- Excellent verbal and written communication skills in English.
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow and Scikit-learn, as well as Hugging Face ecosystem.
- Strong expertise in NLP techniques, including Tokenization and Named Entity Recognition (NER).
- Proficiency using Pandas and Polars, along with experience in building interactive data applications and prototypes using Streamlit.
- Solid experience with tools such as FastAPI, Celery and Keycloak, as well as working with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) pipelines.
- Familiarity with MLflow and Kubeflow.
