Data Science

Role Overview: You should have at least 8 years of hands-on experience as an AI Engineer, Machine Learning Engineer, or a similar role focused on building and deploying AI/ML solutions. Your main responsibilities will involve working with Python and its relevant ML/data science libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch. You should also have extensive experience with at least one major deep learning framework like TensorFlow, PyTorch, or Keras. A solid understanding of machine learning principles, algorithms (e.g., regression, classification, clustering, ensemble methods), and statistical modeling is essential. Additionally, experience with cloud platforms such as AWS, Azure, GCP, and their AI/ML services like SageMaker, Azure ML, and Vertex AI will be beneficial. Key Responsibilities: - Utilize your expertise in Python and its relevant ML/data science libraries for developing AI/ML solutions. - Work with deep learning frameworks like TensorFlow, PyTorch, or Keras to build and deploy machine learning models. - Apply machine learning principles, algorithms, and statistical modeling techniques to solve complex problems. - Collaborate with cloud platforms such as AWS, Azure, and GCP to leverage AI/ML services for efficient solution deployment. Qualifications Required: - Bachelor's Degree in a relevant field. - At least 8 years of experience in AI/ML engineering roles. - Proficiency in Python and its libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). - Familiarity with deep learning frameworks (TensorFlow, PyTorch, Keras). - Strong understanding of machine learning principles and statistical modeling. - Experience working with cloud platforms and AI/ML services (AWS, Azure, GCP, SageMaker, Azure ML, Vertex AI). Company Details: No additional details about the company were provided in the job description.,