Principal Applied Scientist

Oracle Oracle · Enterprise · United Arab Emirates

Principal Applied Scientist at Oracle Health & AI, focusing on developing Generative AI-powered solutions for healthcare and enterprise customers. Responsibilities include translating business requirements into AI projects, designing AI solution architectures, developing production code, and leading/mentoring scientists. Requires experience in designing and implementing scalable AI models for production, deep understanding of ML/DL architectures, practical experience with LLM technologies, and experience with LLM frameworks. A strong publication record and experience leading scientists are also required.

What you'd actually do

  1. Develop new healthcare and enterprise services and features leveraging recent advances in generative AI, machine learning and deep learning.
  2. Design and review the architecture of AI solutions, including data, model, training, and evaluation, employing best practices.
  3. Lead and mentor both junior and senior applied scientists.
  4. Develop production code and advocate for the best coding and engineering practices.
  5. Collaborate with product managers to translate business and product requirements into AI projects.

Skills

Required

  • Designing and implementing scalable AI models for production
  • Machine Learning
  • Deep Learning architectures like Transformers
  • training methods
  • optimizers
  • LLM and generative AI technologies
  • parameter-efficient fine-tuning
  • instruction fine-tuning
  • advanced prompt engineering techniques
  • LangChain
  • LlamaIndex
  • VectorStores and Retrievers
  • LLM Cache
  • LLMOps
  • LMQL
  • Guidance
  • designing data collection/annotation solutions
  • systematic evaluation
  • staying up-to-date with the field
  • applying academic advances to solve complex business problems
  • publication record
  • leading senior scientists
  • leading early-career scientists

Nice to have

  • healthcare AI models
  • computer vision
  • multimodal modeling

What the JD emphasized

  • production
  • scalable AI models for production
  • production systems
  • bringing them into production
  • Strong publication record

Other signals

  • Generative AI
  • LLMs
  • production
  • healthcare