Oracle AI Engineer Case Interview: Designing a Secure GenAI + ML Platform on OCI with Autonomous Database

This Oracle-style case simulates a customer-facing AI engineering engagement on Oracle Cloud Infrastructure (OCI). It mirrors real Oracle interview patterns: whiteboard-first systems design, pragmatic depth on data/ML lifecycle, and security/cost trade-offs aligned to enterprise requirements. Scenario: A global enterprise using Oracle Fusion Applications wants a GPT-powered knowledge assistant and predictive analytics service. You must design and defend an end-to-end solution on OCI that: (a) ingests multi-domain ERP/SCM/HR data, (b) enables Retrieval-Augmented Generation (RAG) over sensitive data, and (c) deploys low-latency inference at scale with strong governance, observability, and cost controls. What Oracle assesses: - Mapping business requirements to specific OCI services and Oracle Database capabilities (no hand-wavy vendor-agnostic designs). - Security-by-default thinking: tenancy/compartment layout, IAM policies, network isolation (VCN/subnets/security lists/WAF), encryption with OCI Vault/KMS, data masking/redaction, audit. - Data/ML rigor: feature/data pipelines, eval/metrics, drift detection, and model lifecycle using OCI Data Science and the Accelerated Data Science (ADS) SDK. - Oracle Database fluency: using Autonomous Database/Oracle Database 23ai AI Vector Search for embeddings and hybrid search; SQL competence and partitioning/indexing design. - Production pragmatism: SLAs/SLOs, regional architecture, DR with Autonomous Data Guard, quotas/limits, and explicit cost/performance trade-offs. Case tasks (you will be timeboxed and may sketch or pseudo-code): 1) Architecture (whiteboard): Propose a secure, multi-tier design using: OCI Object Storage (raw docs), OCI Data Integration/Data Flow (ETL/feature jobs), Autonomous Database (JSON + vector store via 23ai AI Vector Search), OCI Generative AI (base LLM + guardrails), OCI Data Science (experiments/model catalog), API Gateway + Functions or OKE for serving, Logging/Monitoring/Alarms, and Vault/KMS. Include compartments, IAM policies, and VCN/subnet layout. 2) RAG design: Choose embedding strategy, chunking, and metadata schema; describe how you’ll populate vectors in Autonomous Database and perform hybrid (semantic + keyword/SQL) retrieval. Discuss prompt templates, grounding, caching, and toxicity/PII filters. 3) MLOps: Outline training/finetuning or prompt-tuning, offline/online evaluation, A/B rollout, feature store options, model registry, and CI/CD for models and infra. Call out lineage and reproducibility using OCI DS/MLflow. 4) SQL/DB mini-task: Sketch SQL for creating a vector index and performing ANN search; propose partitioning, row-level security (e.g., VPD) and data redaction for PII. Explain how you’d monitor query latency and tune indexes. 5) Reliability/cost: Size compute (OCPU/GPU) for ingestion and inference, estimate storage and egress, and propose autoscaling. Provide multi-AD and cross-region DR choices; explain failure modes and blast-radius limits. 6) Security/compliance: Map controls to data classes; show how keys are rotated, secrets managed, and access audited. Address data residency and least-privilege policies. Deliverables during the interview: - A clear architecture diagram (verbal/whiteboard) with named OCI services and networking. - A short justification of LLM/model choices and evaluation plan. - A brief SQL/pseudo-code snippet for vector search and a Python sketch using ADS to generate/store embeddings. - A risk register (top 3 risks) with mitigations and cost levers. Evaluation rubric (Oracle-specific): - Customer focus and requirements capture (clarity, assumptions, success criteria). - Correct and detailed mapping to OCI and Autonomous Database features; concrete security controls. - Sound ML/RAG design with measurable evaluation and safe-guarding. - Operational readiness: observability, DR, quotas/limits, and cost transparency. - Communication: structured trade-off reasoning, crisp diagrams, and time management. Timeboxing guide (used by interviewers): 10 min requirements + constraints, 30 min architecture/RAG/DB design, 15 min security/reliability/cost, 10 min deep dive Q&A, 10 min wrap-up with risks and next steps.