Machine Learning Engineer

Job Title: Machine Learning Engineer - remote All the relevant skills, qualifications and experience that a successful applicant will need are listed in the following description. Pay Rate: Open to Both C2C and W2 options Position Type: Multiyear Contract Candidates must have a BS in computer science with at least 8 years of professional experience with neural networks, Tensorflow and PyTorch. 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.