This role focuses on architecting and deploying cutting-edge AI-native solutions within the Oracle ecosystem. You will leverage Oracle Cloud Infrastructure (OCI) and Oracle Cloud Visual Builder to create bespoke AI applications and agents, ensuring they are precisely tailored to customer data and specific business processes. The aim is to deliver powerful, AI-driven enhancements to Oracle environments.

Key to this role is the ability to build and run sophisticated AI solutions. This involves integrating Oracle's AI services and data platforms to augment existing Oracle Cloud Visual Builder scenarios. The position demands a strong understanding of AI development lifecycles and the deployment of robust, secure, and production-ready applications.

Design and construct custom Oracle AI solutions, specifically integrating with Oracle Cloud Visual Builder scenarios using OCI AI services and Oracle data platforms. Implement end-to-end Retrieval-Augmented Generation (RAG) and agent patterns, covering ingestion, chunking, embedding, vector search, reranking, and grounded generation with citations.

Establish rigorous evaluation metrics and quality gates, including grounding checks, regression tests, and red-teaming prompts, ensuring seamless integration into CI/CD pipelines before deployment. Engineer secure-by-design deployments, meticulously managing OCI IAM policies, network isolation, secrets, and audit logging to meet production standards. Maintain and operate AI applications in production, focusing on Service Level Objectives (SLOs), comprehensive monitoring (latency, errors, token/cost), incident response, and ongoing optimization efforts.

A minimum of 3 years of experience is required for this role. Candidates should possess foundational knowledge in OCI Generative AI, Data Science, vector databases, and embeddings. Proficiency in Oracle data integration and governance, including PII classification, access control, and lineage, is essential.

Strong engineering skills within the Oracle Cloud Visual Builder domain are necessary to ensure AI outputs are accurately grounded in Oracle process and data contexts. Experience with LLMOps is crucial, encompassing prompt/version control, evaluation harnesses, monitoring, and FinOps cost controls. The ability to take a Proof of Concept (PoC) through to production, ensuring governance and reliability, is expected at the given level.