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Mid-level ML role with popular title, broad GenAI/NLP requirements, and a recognizable but non-FAANG brand.
Core ML/LLM skills are broadly transferable across industries despite Springer Nature's publishing domain.
Explicit 3+ years plus mandatory ML frameworks, GenAI/LLM, NLP, and cloud deployment experience.
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Develop, deploy, and maintain end-to-end AI/ML solutions to improve operational efficiency and decision-making in publishing.
Ensure system scalability, reliability, and data quality while optimizing model performance in production environments.
Conduct research on latest AI/ML and Generative AI technologies, apply techniques in NLP, computer vision, predictive analytics, and communicate insights to non-technical stakeholders.
Bachelor’s or master’s degree in computer science, engineering, or a related field.
3+ years of experience in AI/ML engineering with strong knowledge of machine learning algorithms and deep learning frameworks.
Proficiency in Python or R, and experience with TensorFlow, PyTorch, or scikit-learn.
Experience with cloud platforms such as AWS, Azure, or Google Cloud for AI/ML deployment.
Experienced with Generative AI, large language models (LLM), and building retrieval-augmented generation (RAG) applications.
Operates effectively across end-to-end AI/ML workflows including data preprocessing, feature engineering, modeling, deployment, and monitoring.
Comfortable communicating technical AI/ML findings in ways accessible to non-technical business stakeholders.