Machine Learning Engineer-(Hybrid)

4 days ago


Vienna, United States Shuvel Digital Full time

Responsibility:

  • Build and enhance machine learning models through all phases of development including design, training, validation, and implementation etc.

  • Unlock insights by analyzing large scale of complex numerical and textual data and identifying trends.

  • Partner with a cross-functional team of data engineers, data scientists, and data visualization to deliver projects.

  • Research and evaluate emerging technologies.

  • Develop data science solutions based on tools and cloud computing infrastructure.

  • Perform other duties as assigned.

Qualifications:

  • Bachelor's degree in computer science, mathematics, physics, statistics, or related field.

  • Strong experience with applying expertise in model design, training, validation, and monitoring.

  • Excellent understanding of machine learning, statistical modeling, and algorithms as well as their benefits and drawbacks.

  • Advanced skills with Python, Jupyter Notebook/Jupyter Lab, Visual Studio Code and other languages appropriate for large data analysis.

  • Experience with cloud computing infrastructure.

  • Advanced SQL skills.

  • Experience with data visualization concepts and tools.

  • Ability to convey complex business problems to technical solutions.

  • Ability to work individually, and as part of a team.

  • Advanced verbal, written, interpersonal, and presentation skills to communicate clearly and concisely technical and non-technical information to all levels of management.

Desired:

  • Advanced degree in in computer science, mathematics, physics, statistics, or related field.

  • Experience with Natural Language Processing.

  • Experience with deep learning framework and infrastructure like TensorFlow or PyTorch.

  • Experience and/or willing to learn techniques in Large Language Models (LLMs) and Generative AI.

  • A.I. Model Optimization on GPU architecture. Leveraging C++, CUDA.

  • Experience and/or willing to research, develop, implement, and fine-tuning LLMs in terms of specific domains knowledge and user cases.

  • Knowledge of Machine Learning Ops and CI/CD tools for automation of build, test, and deploy models in production environments.



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