Principal Software Developer

Oracle Oracle · Enterprise · United States

Principal Software Developer to design and develop scalable data pipelines and AI-driven workflows, build LLM/agent-based solutions for business use cases, own end-to-end features, and optimize systems for performance, scale, and low latency. Requires strong software engineering and data engineering skills, cloud experience, and experience with LLMs and agent frameworks.

What you'd actually do

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

Skills

Required

  • BS/MS in Computer Science or equivalent
  • 8+ years of relevant software engineering experience
  • Python/Java
  • SQL
  • Data engineering: ETL, data transformation, data modeling (Spark, SQL)
  • High-scale distributed data systems
  • Cloud experience (OCI/AWS/Azure)
  • Technical Lead / System Design
  • LLMs, prompt engineering, and agent frameworks
  • Blending hands-on coding with AI-driven solutions

Nice to have

  • agentic architectures or GenAI platforms
  • healthcare or digital health systems
  • EHR systems and 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)
  • Demonstrated competence as a Technical Lead / System Design of a non-trivial SaaS/IaaS project spanning multiple functional areas.
  • 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

  • LLM/agent-based solutions
  • end-to-end features
  • low latency
  • LLMs, prompt engineering, and agent frameworks
  • blending hands-on coding with smart adoption of AI-driven solutions