Senior Core Infrastructure Engineer

What you'll do

• Integrate Oracle data platforms to move training and inference data across customer and internal pipelines.
• Build and extend GPU schedulers and capacity-aware placement logic for large-scale GPU fleets.
• Develop orchestration pipelines for training, fine-tuning, and inference workloads using Kubernetes, Argo, and Slurm.
• Ship MLOps agentic tooling for observability, automated triage, cost management, and SLO tracking.
• Convert field requirements from partners into reusable production components rather than one-off scripts.
• Build the execution layer for OCI’s AI and GPU growth motion across cluster fleets.

What we're looking for

• 3–6 years of software engineering experience in distributed systems, data platforms, or ML infrastructure.
• Strong Python skills are required.
• Working knowledge of Go or Rust (preferred).
• Hands-on experience with Kubernetes, container runtimes, and at least one workflow engine like Argo, Airflow, Prefect, or Temporal.
• Familiarity with GPU workloads, CUDA toolchains, or inference serving frameworks such as vLLM, TensorRT-LLM, or Triton.
• Experience with LLM agent frameworks like LangGraph or CrewAI (preferred).
• Prior work on multi-cloud or hybrid deployments (preferred).
• Exposure to QEC, quantum-classical hybrid workloads, or scientific computing (preferred).