Required Skills:

📍AI and Machine Learning Expertise: Deep understanding of machine learning algorithms, such as decision trees, neural networks, clustering, and reinforcement learning. You’re experienced in developing models for NLP, computer vision, and predictive analytics. 📍Programming and AI Tools: Proficiency in programming languages like Python or R, and experience using machine learning frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn. You’re comfortable with coding and debugging AI solutions. 📍Data Engineering and Feature Engineering: Hands-on experience with data preprocessing, feature selection, and engineering for AI models. You know how to clean and transform large datasets to support machine learning workflows. 📍Deployment and Integration: Experience deploying AI models into production environments using cloud platforms (AWS, GCP, Azure) and containerization tools like Docker and Kubernetes. You know how to integrate models into existing systems and optimize for scalability. 📍Collaboration and Communication: Strong collaboration skills, with the ability to work with cross-functional teams, including data scientists, engineers, and product managers. You can clearly communicate technical concepts to non-technical stakeholders.

Educational Requirements:

📍Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Machine Learning, or a related field. Equivalent experience in AI development is also highly valued. 📍Certifications or additional coursework in machine learning, AI, or data science are a plus.

Experience Requirements:

📍3+ years of experience in AI engineering or machine learning, with hands-on experience developing and deploying AI models in real-world applications. 📍Proven track record of working with large datasets, designing machine learning pipelines, and delivering AI-driven solutions that solve business problems. 📍Experience with cloud-based AI services (AWS SageMaker, Google AI Platform, Azure ML) is highly desirable.