AI/ML Engineer

Role Overview: You will be part of the Oracle Cloud Infrastructure (OCI) team, reimagining operations for the enterprise cloud by building cutting-edge AI systems. As an ML Engineer, you will drive the transformation of cloud operations by developing intelligent AI solutions that proactively manage cloud environments, boost operational excellence, and enhance the customer experience. Key Responsibilities: - Lead the development of AI agents tailored for cloud operations, ensuring scalability, reliability, and alignment with customer needs. - Architect and implement tools and frameworks to accelerate AI agent development, experimentation, deployment, and monitoring. - Design robust methodologies to evaluate agent performance, safety, and accuracy in diverse operational scenarios. - Develop advanced meta prompting techniques to dynamically adapt prompts and maximize utility in real-time operational contexts. - Integrate cutting-edge LLM technologies into OCI systems, leveraging fine-tuning, retrieval-augmented generation (RAG), and other advanced techniques. - Collaborate with product, UX, and engineering teams to align technical solutions with business priorities and user experience. - Engage directly with enterprise customers to gather requirements, validate solutions, and ensure agent effectiveness in production environments. - Mentor engineers, establish best practices in AI agent engineering, and contribute to the technical vision and roadmaps. Qualifications Required: - Overall 5+ years of experience with 2+ years in machine learning engineering, focusing on building AI agents and production-grade ML systems. - Expertise in large language models (LLMs), transformers, and GenAI technologies. - Experience in prompt engineering and developing prompting strategies for LLM-based applications. - Ability to evaluate and optimize AI agent performance in real-world, mission-critical environments. - Proficiency in Python programming and practical experience with ML frameworks such as PyTorch or TensorFlow. - Understanding of MLOps practices, including model deployment, scalability, observability, and lifecycle management. - Strong problem-solving skills with a proactive approach to execution. - Capability to communicate effectively with technical leaders, executives, enterprise customers, and cross-org teams. - Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field. Additional Company Details (if available): N/A,