Principal Ai/ ML Ops Engineer

Oracle Oracle · Enterprise · United States

This Principal AI/ML Ops Engineer role at Oracle Health focuses on building high-scale, cloud-based data processing pipelines and AI-driven workflows using LLMs and AI agents to modernize healthcare systems. The role involves designing and developing these systems, optimizing them for performance and low latency, and owning features end-to-end, with a secondary focus on data engineering aspects.

What you'd actually do

  1. Design and develop scalable data pipelines and AI-driven workflows.
  2. Build LLM/agent-based solutions for business use cases (revenue leakage, readmissions, automation).
  3. Own end-to-end features from data ingestion through transformation and on to insights.
  4. Optimize systems for performance, scale, and low latency.
  5. Mentor junior engineers and contribute to design decisions.

Skills

Required

  • Python/Java
  • SQL
  • ETL
  • data transformation
  • data modeling
  • Spark
  • Cloud experience (OCI/AWS/Azure)
  • Technical Lead / System Design
  • LLMs
  • prompt engineering
  • agent frameworks

Nice to have

  • agentic architectures
  • GenAI platforms
  • healthcare or digital health systems
  • EHR systems
  • RCM workflows
  • healthcare coding standards (ICD/CPT)

What the JD emphasized

  • 8+ years of relevant software engineering experience
  • Deep expertise in data engineering: ETL, data transformation, data modeling (Spark, SQL)
  • Experience building high-scale distributed data systems
  • Demonstrated competence as a Technical Lead / System Design of a non-trivial SaaS/IaaS project spanning multiple functional areas
  • Demonstrated competence in taking ambiguous functional and/or product requirements and partitioning them based on functional alignment
  • Experience with owning all aspects of the development, characterization and deployment of features spanning multiple components
  • Experience with LLMs, prompt engineering, and agent frameworks
  • Experience with blending hands-on coding with smart adoption of AI-driven solutions to rapidly prototype, test, iterate, and deliver reliable code

Other signals

  • modernizing Electronic Health Record and Revenue Cycle Management systems using LLMs and AI agents
  • design and build high-scale, cloud-based data processing pipelines that ingest, transform, and analyze massive volumes of healthcare data with low latency, powering business insights and analytics
  • leverage LLMs, AI agents, and modern data platforms to solve problems like clinical decision support, revenue optimization, and workflow automation