You’ll collaborate across engineering, field, and product teams to prototype, test, and publish new labs and reusable assets that power lab events, workshops, and technical enablement programs across North America.
What you’ll do
- Design and build new Oracle-based labs and demos that showcase AI/ML, data, and application services through realistic, scenario-driven use cases (e.g., RAG pipelines, AI agents, or data ingestion workflows).
- Write production-grade code (Python, Bash, Terraform, SQL, etc.) to implement backend logic, automation, APIs, and orchestration used within labs.
- Develop interactive Markdown-based lab instructions, templates, and reusable modules that deliver a consistent, high-quality learner experience.
- Break down complex Oracle and AI concepts into clear, incremental steps that can be easily followed by users of varying technical skill levels.
- Prototype, deploy, and validate labs end-to-end —focusing on usability, reliability, performance, and documentation quality.
- Collaborate with product and field teams to align labs with new Oracle releases, GenAI platform features, and customer-requested topics.
- Build and maintain reference architectures and SDK examples that demonstrate integration of Oracle GenAI Services, AI Vector Search, and Oracle Database.
- Participate in live demos, dry runs, and internal enablement to showcase labs and gather feedback from engineers and customers.
- Mentor junior Cloud Engineers and guide best practices for lab development, testing, and delivery.
Core focus areas
- Lab & Demo Development: workshop authoring, event code setup, Terraform automation, Oracle SDK integration, markdown-based instruction design.
- AI/GenAI Workloads: RAG pipelines, embeddings/vector search, agentic workflows, and GenAI API orchestration.
- OCI Services: Data Flow (Spark), Streaming/Kafka, Object Storage, Functions, OKE, AI Vector Search, IAM/Vault, and networking.
- Oracle Database Integration: Autonomous Database, JSON Duality Views, AI Vector Search, and data ingestion for AI workloads.
- Multi-Cloud & Partner Ecosystem: Familiarity with AWS, Azure, GCP, and NVIDIA AI/cloud offerings for comparative lab design and integration testing.
- Automation & Observability: Build, deploy, and monitor labs with robust logging, telemetry, and rollback capabilities.
