Sr Principal AI Software Engineer - ML & AI Innovation

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 s 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.

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