Lead Principal Platform Software Engineer

Oracle•United States,

7d•$113,600 - $306,400

About The Position

Oracle Health & AI is a new organization focused on product development and strategy for Oracle Health and on building a platform for modernized, automated healthcare. The team is described as a newly established line of business with an entrepreneurial, energetic, and creative environment. The Lead Principal Platform Software Engineer will architect, design, develop, deploy, and operate production-grade AI systems using LLMs, agents, retrieval, and enterprise data. The role builds agentic AI, RAG, search, ranking, knowledge systems, and LLM-powered applications, and optimizes them for quality, latency, reliability, scalability, observability, and cost. It also establishes evaluation and experimentation approaches, builds cloud-native services and distributed systems, and supports asynchronous and event-driven architectures. The engineer works with applied scientists, engineers, product managers, and domain experts, and provides technical leadership through architecture decisions, mentorship, and engineering standards.

Requirements

12+ years of professional experience in software engineering, machine learning engineering, applied machine learning, or related fields.
BS, MS, PhD in Computer Science, Engineering, Machine Learning, or Artificial Intelligence, or equivalent practical experience.
Proven experience designing, building, and operating production ML, AI, LLM, RAG, search, recommendation, conversational AI, or agentic AI systems at scale.
Strong software engineering fundamentals, including distributed systems, concurrency, APIs, data structures, algorithms, testing, debugging, and production operations.
Proficiency in Python and Java, with experience developing and maintaining production software in both languages.
Hands-on experience building scalable backend services, cloud-native systems, APIs, distributed applications, or model-serving platforms.
Experience with modern LLM ecosystems, including prompt engineering, tool calling, retrieval-augmented generation, model serving, evaluation, and deployment.
Experience building search, retrieval, ranking, vector database, or enterprise knowledge systems.
Experience with asynchronous communication patterns, message queues, pub/sub systems, data streaming platforms, or event-driven architectures.
Experience with containerized applications and Kubernetes-based deployments.
Strong technical judgment and ability to balance quality, latency, reliability, scalability, and cost tradeoffs.
Demonstrated ownership of complex technical problems from architecture through production operation.
Strong communication skills and experience working across engineering, product, and applied science teams.

Nice To Haves

Experience building agentic AI systems involving planning, tool orchestration, memory, autonomous workflows, or multi-agent architectures.
Experience with LangGraph, LangChain, LlamaIndex, Semantic Kernel, MCP, or equivalent orchestration technologies.
Experience with retrieval and ranking technologies such as Elasticsearch, OpenSearch, vector databases, hybrid search, reranking, and query understanding systems.
Experience designing and operating production LLM serving platforms with responsibility for latency, throughput, reliability, scalability, observability, and cost efficiency.
Experience with model adaptation techniques such as fine-tuning, LoRA, distillation, preference optimization, or domain adaptation.
Experience building evaluation systems, A/B testing frameworks, human-in-the-loop review processes, and AI quality measurement platforms.
Experience with OCI, AWS, Azure, or GCP and technologies such as Docker, Kubernetes, Kafka, Spark, or equivalent distributed systems.
Experience developing tools, frameworks, APIs, or platforms used by applied scientists, data scientists, or machine learning engineers.
Experience building AI systems in healthcare, enterprise SaaS, regulated environments, or other privacy-sensitive domains.
Demonstrated technical leadership through architecture ownership, mentoring, cross-functional collaboration, and delivery of high-impact initiatives.

Responsibilities

Architect, design, develop, deploy, and operate production-grade AI systems powered by LLMs, agents, retrieval, and enterprise data.
Build agentic AI systems using tool use, memory, planning, orchestration, and workflow automation.
Design and implement scalable RAG, search, retrieval, ranking, and knowledge systems across structured and unstructured data sources.
Develop LLM-powered applications incorporating prompt and context engineering, tool integrations, model routing, guardrails, and workflow orchestration.
Optimize AI systems for quality, latency, reliability, scalability, observability, and cost efficiency.
Establish evaluation frameworks, experimentation platforms, and feedback loops to improve AI system performance and user outcomes.
Build and maintain cloud-native services, APIs, SDKs, and distributed systems that support reliable development and operation of AI capabilities.
Design and support asynchronous communication patterns, event-driven architectures, and workflow orchestration using messaging and streaming technologies.
Partner with applied scientists, engineers, product managers, and domain experts to translate ambiguous requirements into scalable technical solutions.
Drive architecture decisions, mentor engineers, and raise engineering standards across the organization.

Benefits

Medical, dental, and vision insurance, including expert medical opinion.
Short-term and long-term disability insurance.
Life insurance and AD&D.
Supplemental life insurance for employees, spouses, and children.
Health care and dependent care Flexible Spending Accounts.
Pre-tax commuter and parking benefits.
401(k) Savings and Investment Plan with company match.
Flexible or accrued paid vacation, depending on employee eligibility and position.
11 paid holidays.
Paid sick leave: 72 hours upon hire, refreshed each calendar year, with unused hours carrying over up to a maximum of 112 hours.
Paid parental leave.
Adoption assistance.
Employee Stock Purchase Plan.
Financial planning and group legal benefits.
Voluntary auto, homeowner, and pet insurance.
Potential eligibility for bonus, equity, and compensation deferral.
Volunteer programs.