O

Oracle

Builds databases, business apps and cloud services

Lead Principal Platform Software Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeUnited StatesIn-person

About the Job

Requirements

  • 12+ years of professional experience in software engineering, machine learning engineering, applied machine learning, or related fields.
  • A BS, MS, or PhD in Computer Science, Engineering, Machine Learning, or Artificial Intelligence, or equivalent practical experience.
  • A proven track record of 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 the 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 effectively across engineering, product, and applied science teams.

Responsibilities

  • Architect, design, develop, deploy, and operate production-grade AI systems powered by LLMs, agents, retrieval, and enterprise data.
  • Build agentic AI systems that leverage tool use, memory, planning, orchestration, and workflow automation to solve complex business problems.
  • Design and implement scalable RAG, search, retrieval, ranking, and knowledge systems across structured and unstructured data sources.
  • Develop LLM-powered applications, including 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 continuously improve AI system performance and user outcomes.
  • Build and maintain cloud-native services, APIs, SDKs, and distributed systems that enable 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 closely 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.

Desired Qualifications

  • Experience building agentic AI systems involving planning, tool orchestration, memory, autonomous workflows, or multi-agent architectures.
  • Experience with frameworks and platforms such as 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.

About the Company

Oracle

Builds databases, business apps and cloud services

Oracle builds databases, business applications, and cloud infrastructure for companies and public-sector organizations. Oracle Database stores and processes business data, while Oracle Cloud Infrastructure provides computing, storage, and networking for applications and AI workloads. Fusion Cloud Applications support finance, human resources, supply chains, and customer service, helping businesses manage everyday operations and reporting. Customers pay for cloud subscriptions and usage, software licenses, support, hardware, and consulting, with deployment options in Oracle's cloud or their own data centers. Oracle was founded in 1977 and is headquartered in Austin, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Austin, TX
Founded
1977

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