Principal Software Engineer - AI & ML Innovation at Oracle Risk Management Services

Seattle, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

23 Jan, 26

Salary

0.0

Posted On

26 Oct, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI, ML, Deep Learning, Kubernetes, Python, Go, Containerization, Distributed Systems, Parallel Computing, Generative AI, Large Language Models, Model Fine-Tuning, Retrieval-Augmented Generation, Scalable Orchestration, GPU-Accelerated Services, Observability Frameworks

Industry

IT Services and IT Consulting

Description

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.

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Responsibilities

The role involves evaluating, integrating, and optimizing technologies for AI/ML infrastructure, focusing on low latency and high throughput. The engineer will also guide strategic decisions and lead initiatives in AI systems design.