*Please note this is an in-office role in Oracles Redwood City CA office.
AI/ML Engineer with hands-on experience to build production-grade multi-agent AI applications Agentic RAG knowledge assistants and scalable ML pipelines. candidate should have strong applied AI background in distributed reasoning retrieval-augmented architectures and cloud-native deployment (Kubernetes Docker FastAPI) plus data science experience with large-scale consumer data modeling. good to have experience experience with impact improving reliability latency and model quality (e.g. reduced hallucinations faster inference higher multi-hop accuracy)
As part of Oracle Applied AI team you will work on below list of technologies
LLM & Agentic Systems: Multi-agent orchestration tool use routing verification/refinement loops; ReAct/CoT prompting and synthetic data generation; self-consistency distillation
RAG / Knowledge Automation: GraphRAG vector DBs (Qdrant Chroma) semantic chunking query rewriting reranking grounded response generation
Model Training & Optimization: PEFT/LoRA BitsAndBytes quantization ONNX Runtime optimization; experimentation tracking and reproducibility (Weights & Biases MLflow)
Data Science & ML at Scale: PySpark pipelines feature selection (PCA clustering XGBoost-based screening RFE) OOT validation drift monitoring (PSI/IV SHAP shift) AUROC optimization
Backend / Cloud-Native Engineering: FastAPI Pydantic gRPC/protobuf microservices patterns Kubernetes/Docker service mesh (Istio) CI/CD and monitoring (Prometheus/Grafana)
Career Level - IC4
Required Experience:
IC
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