The Senior Principal AI/ML Software Engineer is responsible for evaluating, integrating, and optimizing cutting-edge technologies for AI/ML infrastructure, focusing on achieving low latency, high throughput, and efficient resource utilization for both model training and inference at scale. This role guides key strategic decisions related to Oracle Cloud’s AI infrastructure offerings, spearheads the design and implementation of scalable orchestration for AI/ML workloads—incorporating the latest research in generative AI and large language models—and leads initiatives such as Retrieval-Augmented Generation and model fine-tuning. The ideal candidate will design and develop scalable, GPU-accelerated AI services using tools like Kubernetes and Python/Go, and must possess strong programming skills, deep expertise in deep learning frameworks, containerization, distributed systems, and parallel computing, along with a comprehensive understanding of end-to-end AI/ML workflows.

 

Responsibilities

Evaluate, Integrate, and Optimize state-of-the-art technologies across the stack, for latency, throughput, and resource utilization for training and inference workloads.  Guide strategic decisions around Oracle Cloud’s AI Infra offerings 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. 

 

Qualifications: 

Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Machine Learning, or a related field (or equivalent experience).  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.