What You’ll Do

Capacity Engineering – Act as a strategic capacity partner, immersing in the end-to-end architecture and performance of SaaS production services. Ensure mission-critical workloads—including emerging agentic AI and MLOps pipelines—are forecasted, scaled, and optimized for OCI cloud capacity at enterprise scale. Cost Engineering – Translate SaaS capacity architectures into cost models that improve efficiency year over year. Partner with Cost Engineers to drive down infrastructure margins while enhancing reliability, producing actionable forecasts and executive-level insights. AI/MLOps & Automation – Apply deep knowledge of AI, MLOps, and orchestration to streamline operations, eliminate technical debt, and propose automation opportunities. Collaborate with AI/ML Ops and data engineering teams to evolve architectures, enhance scalability, and influence future OCI feature sets. Run-the-Business Support – Deliver detailed capacity roadmaps that define tuning, scaling, and demand characteristics. Communicate inflection points and future requirements to the Cloud Capacity Run-the-Business organization for seamless planning. Technical Expertise – Leverage a strong foundation in cloud capacity topologies (compute, storage, network) to identify dependencies and drive service reliability improvements. Prior experience across DB, middleware, containers, or networking is valuable in translating complex architectures into capacity supply requirements. Cross-Team Collaboration – Engage confidently across all levels of the organization, from ICs to executives, as a trusted advisor on SaaS capacity. Present data-driven insights with clarity and executive presence. Curiosity & Breadth – Approach services with professional curiosity, exploring APIs, profiling workloads, and analyzing anomalies to anticipate demand and performance needs.

Your Experience

Bachelor’s degree in Computer Science or related field; Master’s preferred Relevant Cloud MLOps / AI certifications (e.g., AWS ML Specialty, GCP ML Engineer, Azure AI, NVIDIA MLOps, Linux Foundation MLOps Practitioner) 10+ years senior engineering experience across one or more domains: databases (Oracle DB preferred), virtualization/middleware, container orchestration, networking, or monitoring/observability Proven expertise in forecasting, scaling, and cost-optimizing capacity for AI/ML and MLOps workloads, including dynamic and agentic workloads, across hybrid and cloud environments Strong knowledge of Oracle OCI cloud services Advanced analytical skills with experience building and interpreting complex models (Excel or equivalent) Exceptional communication and stakeholder-management skills; ability to translate engineering into executive-ready narratives Experience driving initiatives in fast-paced, dynamic, cross-functional environments