Responsibilities:
• Design, build, and deploy machine learning models and systems that deliver real-world business value.
• Collaborate with data scientists, engineers, and stakeholders to identify opportunities and implement machine learning solutions across various industries.
• Develop and optimize algorithms for large-scale data processing and model training.
• Build scalable data pipelines to support machine learning workflows and ensure efficient data flow.
• Continuously evaluate, test, and improve the performance of deployed machine learning models.
• Create and maintain robust, reproducible, and production-ready code for machine learning applications.
• Contribute to the research and development of innovative machine learning methodologies and approaches.
• Write clear, concise documentation for models, algorithms, and workflows to ensure smooth collaboration across teams.
Requirements:
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. PhD is a plus.
• Strong experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn).
• Proficiency in programming languages such as Python, C++, or Java, with experience in building production-grade code.
• Solid understanding of machine learning algorithms, statistical modeling, and deep learning techniques.
• Experience with cloud platforms (e.g., AWS, GCP, Azure) for deploying and scaling machine learning models.
• Familiarity with version control systems (e.g., Git) and collaboration tools for team-based development.
• Strong problem-solving skills with the ability to approach challenges systematically and innovatively.
• Experience with data preprocessing, feature engineering, and model tuning to optimize performance.
• Excellent communication skills to explain complex technical concepts and results to non-technical stakeholders.
• Ability to work independently and collaboratively in a fast-paced, results-driven environment.