Data Engineer III
oraclecloud
Job Description
- Design and build ML systems end-to-end: problem framing, data prep, feature engineering, model training, evaluation, deployment, monitoring, and iteration.
- Develop agentic AI solutions: LLM agents that plan, call tools/APIs, run multi-step workflows, and apply guardrails/fail-safes.
- Implement RAG capabilities: retrieval strategy, chunking, embeddings, indexing, and re-ranking to ground agent responses in knowledge.
- Build full-stack product experiences for ML/agent systems: backend services plus frontend UIs for configuration, human-in-the-loop review, observability, and workflow execution.
- Develop and integrate APIs/services: design and implement RESTful (and/or event-driven) integrations to serve models, agents, features, and data products.
- Build scalable data pipelines with Apache Spark (PySpark/Scala) for batch processing and feature generation.
- Use SQL extensively for exploration, transformations, validation checks, and query performance tuning on large datasets.
- Operationalize and evaluate ML/LLM systems with MLOps: CI/CD, model registry, experiment tracking, reproducible training, automated evaluation/regression tests, and quality frameworks (offline metrics + HITL).
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.