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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Principal Platform Software Engineer - **Company:** Oracle - **Location:** Montpelier, VT, United States - **Experience:** Expert - **Salary:** $113,600.0 - $306,400.0 - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Cerner, Java (Programming Language), A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Databases, Data Structures, Software Debugging, Distributed Systems, Elasticsearch, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Oracle (Applications), Queueing Systems, Software Engineering, Data Streaming, System Software, Workflow Management Systems, Chatbots, LangChain, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Multi-Agent Systems, Concurrency, Prompt Engineering, Apache Spark, Llamaindex, Backend, Agentic-AI, Event Driven Architecture, Containerization, Kubernetes, Information Technology, Low Latency, Apache Kafka, Machine Learning Operations, Video Streaming, Asynchronous Programming, Semantic Kernel, Oracle Cloud Infrastructure, Serverless Computing, Human in the Loop, OpenSearch, Docker - **Published:** November 2, 2026 - **Apply:** https://dejobs.org/x/x/02D9FA394EA6458BAA9E1F94DD41D388/job/ ## About the Role * 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, Artificial Intelligence, or equivalent practical experience. * 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., * 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. ## Description Building off our Cloud momentum, Oracle has formed a new organization - Oracle Health & AI. This team will focus on product development and product strategy for Oracle Health, while building out a complete platform supporting modernized, automated healthcare. This is a net new line of business, constructed with an entrepreneurial spirit that promotes an energetic and creative environment. We are unencumbered and will need your contribution to make it a world class engineering center with the focus on excellence. * 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.