AI Analytic Developer

• Due to the client contract, you will be assigned, this position requires you to be a U.S. citizen
• Due to the client contract, you will be assigned, this position requires you to be a U.S. citizen
• Design, build, test, and deploy production AI agents and AI-assisted workflows for analytics and reporting use cases.
• Implement agents that support tasks such as SQL generation, pipeline creation, metric design support, insight discovery, executive narrative generation, and dashboard/report scaffolding.
• Integrate AI capabilities with enterprise tools, data platforms, semantic services, and existing engineering workflows.
• Develop structured tool-calling patterns, validation logic, retry handling, and grounded output generation.
• Build evaluation and testing approaches for AI workflows, including regression testing, quality validation, and failure analysis.
• Partner with architects, semantic engineers, and domain teams to ensure AI outputs are useful, accurate, and operationally practical.
• Optimize workflows for quality, speed, cost, and maintainability.
• Contribute to AI engineering standards, reusable components, and continuous improvement across the team.
• 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.
• 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.

None flagged.