Principal Member Technical Staff

Oracle Oracle · Enterprise · Seattle, WA +1

This Principal Member Technical Staff role at Oracle focuses on leveraging AI/ML to enhance cloud infrastructure and automation. The responsibilities include designing and implementing ML algorithms for failure detection, developing NLP/ML-powered ticket routing frameworks, and creating AI/ML tools for automated testing and incident reproduction. The role requires expertise in distributed systems, virtualized infrastructure, and highly available services, with a strong emphasis on applying AI/ML techniques to solve complex operational challenges within Oracle's cloud ecosystem.

What you'd actually do

  1. Design and implement spike detection mechanisms for provisioning failures to minimize operational disruptions using ML algorithms.
  2. Expand integrations with Kafka to enable near real-time actions supporting 1-Day SLO objectives for hardware repairs, utilizing event-driven architecture and stream processing.
  3. Developing an automated ticket routing framework to streamline workflows, enhance efficiency, and reduce operational overhead, powered by NLP and ML.
  4. Accelerate dedicated initiatives through collaborative efforts with cross-functional teams and customers, applying AI-driven insights and recommendations.
  5. Harness the power of AI and ML to create innovative tools and frameworks that automate testing, simulate complex environments, and reproduce incidents, freeing up human ingenuity to focus on higher-value tasks and amplifying our ability to deliver exceptional customer experiences.

Skills

Required

  • Python
  • Java
  • TypeScript
  • Kafka
  • Agile Principles
  • OCI
  • AWS
  • Azure
  • Google Cloud Platform (GCP)
  • Linux
  • Docker
  • RESTful APIs
  • TensorFlow
  • PyTorch
  • Keras
  • scikit-learn
  • NLTK
  • spaCy

Nice to have

  • User Experience (UX) design principles
  • Data management: data modeling, data warehousing, data governance
  • Bash
  • Perl
  • Ruby
  • API gateways
  • API security
  • Swagger/OpenAPI
  • chatbots
  • virtual assistants
  • predictive analytics

What the JD emphasized

  • AI and ML techniques
  • AI-driven development
  • AI and ML frameworks
  • AI-powered tools and platforms

Other signals

  • design and implement spike detection mechanisms for provisioning failures to minimize operational disruptions using ML algorithms
  • Developing an automated ticket routing framework to streamline workflows, enhance efficiency, and reduce operational overhead, powered by NLP and ML
  • Harness the power of AI and ML to create innovative tools and frameworks that automate testing, simulate complex environments, and reproduce incidents