Senior Applied Scientist at Oracle Risk Management Services

, , Australia -

Full Time

Start Date

Immediate

Expiry Date

25 Dec, 25

Salary

0.0

Posted On

26 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Generative AI, Large Language Models, Machine Learning, Deep Learning, Prompt Engineering, Data Pipelines, Model Training, Model Evaluation, Production Code Development, Risk Management, Research Excellence, Healthcare AI, Computer Vision, Multimodal Modeling, Data Collection, Annotation Solutions

Industry

IT Services and IT Consulting

Description

Oracle Health AI (OHAI) is Oracle’s center of innovation for applying advanced artificial intelligence to the most critical challenges in healthcare and enterprise. Leveraging Oracle’s extensive cloud resources and rich healthcare data assets, our multidisciplinary team of scientists, engineers, and clinicians develops leading-edge AI solutions -- such as large language models and generative AI -- that drive smarter decisions, automate workflows, and improve outcomes at scale. At OHAI, we are dedicated to translating the latest breakthroughs in AI into practical, responsible, and impactful technology for clinicians, patients, and organizations worldwide. We are seeking a visionary Senior Applied Scientist to lead the design, development, and delivery of scalable, production-ready generative AI solutions in healthcare and enterprise domains. As a senior technical leader, you will collaborate cross-functionally with product, engineering, and clinical teams, leveraging the latest advances in Generative AI. Responsibilities: Project Delivery: Drive projects from concept to production, participating in planning, review, and retrospective meetings. Innovative Solution Design: Research, design, and implement cutting-edge generative AI models—leveraging techniques like LLM parameter-efficient fine-tuning, prompt engineering, and multi-modal modeling. Architecture & Best Practices: Architect robust AI frameworks, covering data pipelines, model training, evaluation, and deployment while ensuring code quality and engineering best practices. Production Code Development: Write and review production-level code, advocating for maintainability and robustness. Risk Management: Proactively identify risks and design mitigation strategies at the intersection of technical and business requirements. Research Excellence: Contribute to the academic and scientific community through publications, reviews, and by leveraging state-of-the-art research in practical applications. Qualifications and Experience: Demonstrated experience in applying LLMs and generative AI technologies to all possible steps in the devops with the goal of improving efficiency in product development. Practical experience with the latest technologies for developing LLMs, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques. Deep technical understanding of Large Language Model, Machine Learning / Deep Learning architectures like Transformers, training methods, and optimizers. Hands-on experience with different LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc. Proven experience in designing data collection/annotation solutions and systematic evaluation necessary for developing and maintaining production systems. Commitment to staying up to date with the field and applying academic advances to solve complex business problems and bringing them into production. Strong publication record, including as a lead author or reviewer, in top-tier journals or conferences. Preferred Qualifications: Knowledge of healthcare and experience delivering healthcare AI models are a significant plus. Familiarity and experience with the latest advancements in computer vision and multimodal modeling is a plus. Education: PhD Computer Science, Mathematics, Statistics, Physics, Linguistics or a related field with a dissertation, thesis or final project centered in Machine Learning and Deep Learning) with 0-2 years relevant experience is preferred but not a must; OR Masters or Bachelor's in related field with 4+ years relevant experience #LI-DNI

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Responsibilities

Lead the design, development, and delivery of scalable, production-ready generative AI solutions in healthcare and enterprise domains. Collaborate cross-functionally with product, engineering, and clinical teams to leverage the latest advances in Generative AI.