Machine Learning Engineer-(Hybrid)

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.