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.
