What are the responsibilities and job description for the MACHINE LEARNING ENGINEER-(HYBRID) position at Shuvel Digital?
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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