• Machine Learning Engineer-(Hybrid)

    Shuvel Digital (Vienna, VA)


    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.