Lead Principal Platform Software Engineer

Oracle Corporation; France; On-site; EUR 140,000 - 210,000; Full time

Job summary

Oracle Health & AI is building a new organization to develop and operate AI-powered healthcare platforms. We seek senior engineers with 12+ years in software and ML, strong Python/Java, and experience in distributed systems, Kubernetes, and LLM ecosystems to deliver production-grade, scalable AI capabilities.

Collaborate with scientists, product managers, and engineers to craft robust, observable systems with strong performance, security, and cost efficiency across cloud-native deployments.

Qualifications

• 12+ years of professional experience in software engineering, ML engineering, or related fields.
• Proven track record of designing, building, and operating production ML/AI systems at scale.
• Proficiency in Python and Java with production software experience.
• Hands-on experience building scalable backend services and cloud-native systems.
• Experience with modern LLM ecosystems, including prompt engineering and model serving.
• Experience with search, retrieval, ranking, and enterprise knowledge systems.

Responsibilities

• Architect, design, develop, deploy, and operate production-grade AI systems powered by LLMs and enterprise data.
• Mentor engineers and drive engineering standards across the organization.
• Partner with scientists, engineers, and product managers to translate requirements into scalable solutions.
• Design and support asynchronous communication patterns and workflow orchestration.
• Build cloud-native services, APIs, and distributed systems enabling reliable AI capabilities.
• Drive architecture decisions and ensure quality, latency, and cost tradeoffs.

Skills

Distributed systems

Python

Java

LLMs

AI systems

Prompt engineering

Tooling and orchestration

Observability

Security

Education

BS/MS/PhD in Computer Science or related field

Tools

Kubernetes

Docker

LangGraph

LangChain

OpenSearch

Kafka

Spark

Job 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.

Internal Responsibilities

Qualifications

• 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.

Preferred 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.

External Responsibilities

Qualifications

• 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.

Preferred 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.