As an AI & Data Architect, you will define the technical architecture for large-scale AI and data platforms that power both OCI services and AI-enabled experiences across Oracle products. You will establish reference architectures, canonical APIs, integration patterns, and platform primitives that product teams can build on rather than recreate independently.
You will work across distributed cloud infrastructure, AI systems, databases, data engineering, search, enterprise applications, networking, security, and developer platforms. The role requires strong systems thinking, deep software and data architecture experience, practical knowledge of modern AI systems, and the ability to influence technical direction across multiple engineering organizations without relying on organizational authority.
The architecture is expected to span OCI and the broader Oracle portfolio. Representative platforms include:
OCI Generative AI (Responses API, hosted agentic applications, and Agents/RAG); OCI Data Science; OCI AI Services (Language, Speech, Vision, and Document Understanding).
Oracle AI Database 26ai / Autonomous AI Database; AI Vector Search; Select AI / NL2SQL; Oracle AI Data Platform; GoldenGate; Object Storage, Streaming, Data Integration, Data Flow, and OpenSearch.
Fusion Cloud Applications; AI Agent Studio / Fusion Agentic Applications; Oracle Analytics Cloud / Fusion Data Intelligence; Oracle Integration; Oracle APEX.
Reusable capabilities for Oracle SaaS and industry products, NetSuite, Oracle Health, partner solutions, and customer applications.
Define long-term architecture and technical strategy for Oracle-wide AI and data platform capabilities, with OCI as the core cloud foundation and clean integration into Oracle Database, Applications, Analytics, and Integration products.
Design scalable platform architectures for generative AI, agentic AI, classical machine learning, structured and unstructured data, enterprise search, knowledge systems, and conversational data access.
Architect systems for:
Foundation-model access, model routing, inference, embeddings, reranking, fine-tuning, prompt/context management, and model lifecycle.
Agent runtimes, multi-agent orchestration, planning, memory, tool execution, workflow integration, human approvals, and secure delegation.
Retrieval-augmented generation (RAG), knowledge bases, semantic and hybrid search, query rewriting, reranking, grounding, provenance, and citations.
Enterprise knowledge management including ingestion, parsing, chunking, metadata, taxonomy, ontology, business glossary, lifecycle, and entitlement-aware retrieval.
NL2SQL and conversational analytics, including schema/semantic grounding, metadata enrichment, SQL generation and validation, permission-aware execution, and natural-language narration of results.
AI-ready data platforms supporting batch, streaming, change data capture, lakehouse patterns, data products, feature/embedding generation, and low-latency serving.
AI evaluation and observability covering quality, hallucination/grounding, retrieval relevance, tool-call success, agent completion, latency, reliability, safety, and cost.
AI security and governance including identity, authorization, tenant isolation, private networking, secrets, auditability, data residency, prompt-injection defenses, exfiltration controls, and policy enforcement.
Multimodal AI experiences spanning text, documents, images, speech, and structured enterprise data.
Developer platforms, APIs, SDKs, reference implementations, and reusable components that let Oracle teams and customers build AI applications consistently.
Define architecture patterns that combine OCI Generative AI and agent capabilities with Oracle AI Database 26ai, Autonomous AI Database, AI Vector Search, Select AI, Oracle AI Data Platform, GoldenGate, OpenSearch, Object Storage, and other OCI data services.
Establish reusable integration patterns between OCI AI services and Oracle Fusion Cloud Applications, AI Agent Studio / Fusion Agentic Applications, Oracle Analytics Cloud, Fusion Data Intelligence, Oracle Integration, and adjacent Oracle products.
Drive semantic architecture across structured data and enterprise knowledge so agents and AI assistants understand business concepts, relationships, permissions, and source-of-truth boundaries rather than only raw schemas or documents.
Define retrieval architecture choices across Oracle AI Database vector search, OCI Search with OpenSearch, managed knowledge bases, file search, and federated enterprise sources, including guidance for hybrid retrieval, ranking, freshness, and ACL enforcement.
Partner with database, data, AI science, applications, analytics, security, and infrastructure teams to operationalize new model capabilities without fragmenting the platform architecture.
Define canonical APIs, schemas, contracts, tool interfaces, event patterns, and interoperability standards for agents, models, knowledge sources, data products, and enterprise applications.
Establish patterns for hybrid and distributed deployments where data or inference must remain close to regulated, sovereign, customer, or on-premises environments.
Evaluate emerging AI, data, search, agent, and model-serving technologies and determine where Oracle should build, integrate, standardize, or partner.
Drive technical direction across multiple engineering organizations and mentor senior engineers and architects.
BS, MS, or PhD in Computer Science, Data/AI, Electrical Engineering, Mathematics, or a related technical field.
15+ years of software engineering experience, with significant experience defining architecture for distributed, data-intensive, or cloud platforms operating at large scale.
Deep expertise in cloud-native architecture, APIs, event-driven systems, microservices, asynchronous workflows, reliability, and multi-tenant platform design.
Strong understanding of modern AI application architecture, including LLMs, embeddings, RAG, agentic systems, tool use, evaluation, and production operationalization.
Strong data architecture background spanning relational and non-relational databases, data lakes/lakehouses, streaming, CDC, search, metadata, governance, and structured/unstructured data.
Experience designing secure enterprise platforms with strong authentication, authorization, distributed identity, policy enforcement, encryption, auditability, and data isolation.
Strong programming background in Java, Python, Go, C++, Rust, or similar languages, with the ability to reason about implementation tradeoffs and platform APIs.
Experience with Kubernetes, containers, Linux, networking, service-to-service security, observability, and modern cloud infrastructure.
Excellent written and verbal communication skills, including the ability to produce architecture documents, reference designs, and clear technical decisions for senior engineering and product audiences.
