Senior Service Reliability Operator (SRO), Cloud Operations

Role Overview: You will work with Oracle's cutting-edge technology to develop, implement, and support Oracle's global infrastructure. As a part of the IT organization, you will assist in analyzing existing complex programs, formulating logic for new internal systems, preparing flowcharting, coding, and testing/debugging programs. Your responsibilities will involve developing conversion and system implementation plans and recommending changes to development, maintenance, and system standards. Key Responsibilities: - Design and implement machine learning (ML) and deep learning models using frameworks like TensorFlow, PyTorch, and Keras to solve real-world problems in areas such as natural language processing (NLP), computer vision, and predictive analytics. - Collect, preprocess, and analyze large datasets to train, validate, and optimize AI models for accuracy and scalability. - Integrate AI solutions into production environments, including API development, cloud deployment (AWS, Azure, GCP, or OCI), and MLOps practices for seamless automation. - Contribute to AI projects, from prototyping to deployment, including documentation of methodologies, algorithms, and processes. - Collaborate with data scientists, software engineers, and stakeholders to align AI initiatives with business goals and ensure ethical AI practices. - Troubleshoot and debug complex AI systems, resolving issues related to model performance, data quality, and integration challenges. Qualifications Required: - Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related field. - 3-5 years of professional experience as an AI Developer, ML Engineer, or similar role, with proven success in deploying AI solutions in production environments. - Strong proficiency in programming languages such as Python (primary), Java, C++, or R. - Hands-on experience with ML frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch) and deep learning techniques (e.g., neural networks, CNNs, RNNs). - Expertise in data manipulation tools like Pandas, NumPy, and SQL/NoSQL databases. - Familiarity with cloud platforms (AWS, Azure, GCP, or OCI) and tools for model deployment (e.g., Docker, Kubernetes, MLflow). - Solid understanding of software engineering principles, including version control (Git), RESTful APIs, and Agile/DevOps methodologies. - Excellent problem-solving skills, with the ability to handle ambiguous problems and thrive in a collaborative, fast-paced setting.,