Oracle Cloud's AI infrastructure offerings need to be optimized for low latency, high throughput, and efficient resource utilization for both model training and inference at scale, incorporating the latest research in generative AI and large language models.
Requirements
- Experience with Machine Learning and Deep Learning concepts, algorithms and models
- Proficiency with orchestration and containerization tools like Kubernetes, Docker, or similar.
- Expertise in modern container networking and storage architecture.
- Expertise in orchestrating, running, and optimizing large-scale distributed training/inference workloads
- Have deep understanding of AI/ML workflows, encompassing data processing, model training, and inference pipelines.
- Experience with parallel computing frameworks and paradigms.
- Strong programming skills and proficiency in major deep learning frameworks.
Responsibilities
- Evaluate, Integrate, and Optimize state-of-the-art technologies across the stack, for latency, throughput, and resource utilization for training and inference workloads.
- Design and implement scalable orchestration for serving and training AI/ML models, Model Parallelism & Performance across the AI/ML Stack
- Explore and incorporate contemporary research on generative AI, agents, and inference systems into the LLM software stack.
- Lead initiatives in Generative AI systems design, including Retrieval-Augmented Generation (RAG) and LLM fine-tuning,
- Design and develop scalable services and tools to support GPU-accelerated AI pipelines, leveraging Kubernetes, Python/Go, and observability frameworks.
Other
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Machine Learning, or a related field (or equivalent experience).
