The Machine Learning Engineer will lead the development of advanced Machine Learning and Artificial Intelligence solutions that support large-scale business applications and customer experiences. The role will involve designing scalable data pipelines using PySpark, developing and deploying production-grade machine learning models using TensorFlow or PyTorch, and translating complex business challenges into data-driven solutions. The ideal candidate will have strong expertise in supervised and unsupervised learning, deep learning architectures, cloud-based machine learning platforms, and production model deployment, while also contributing to technical leadership and mentoring.
Key Responsibilities
- Lead the development of advanced Machine Learning and Artificial Intelligence solutions.
- Design and develop scalable data pipelines using PySpark.
- Develop, train, validate, and deploy production-grade machine learning models.
- Utilize TensorFlow and/or PyTorch for deep learning model development.
- Apply supervised and unsupervised machine learning techniques to solve complex business problems.
- Design and implement deep learning architectures, including CNN, RNN, and LSTM models.
- Develop and optimize machine learning solutions using Scikit-Learn.
- Deploy and manage machine learning models using AWS cloud technologies and SageMaker.
- Translate complex business challenges into scalable, data-driven machine learning solutions.
- Collaborate with data scientists, engineers, and business stakeholders to define and deliver ML/AI initiatives.
- Develop production-ready ML solutions with a focus on scalability, reliability, and performance.
- Mentor team members and contribute technical expertise to strategic data and AI initiatives.
- Stay current with emerging Machine Learning and Artificial Intelligence technologies and practices.
Required Qualifications
- Strong hands-on experience with Python.
- Strong experience with PySpark.
- Hands-on experience with TensorFlow and/or PyTorch.
- Experience with Scikit-Learn.
- Strong experience with AWS and cloud-based machine learning solutions.
- Experience with ML model development and deployment.
- Strong understanding of supervised and unsupervised learning.
- Experience with deep learning architectures such as CNN, RNN, and LSTM.
- Experience developing and deploying production-grade machine learning models.
- Strong analytical, problem-solving, and technical leadership skills.