Principal Core Infrastructure Engineer-Network Capacity

Oracle•Nashville, TN

40d•$114,600 - $234,600

About The Position

Oracle Cloud Infrastructure (OCI) is seeking a principal-level software developer to design and build scalable software capabilities supporting infrastructure demand and capacity planning. The role develops reliable, secure, data-intensive services for engineering and planning teams, collaborating with software developers, machine-learning engineers, data scientists, domain experts, and product stakeholders to translate complex planning requirements into production-quality software. This individual-contributor position carries significant technical ownership and cross-functional influence. The engineer provides technical leadership across architecture, design, implementation, testing, deployment, and operations; resolves ambiguous problems; establishes engineering standards; and helps build systems that operate reliably at cloud scale.

Requirements

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
Eight or more years of professional software-development experience.
Significant experience designing and delivering production software systems.
Strong programming skills in Java, Python, Go, C++, or another modern programming language.
Strong understanding of data structures, algorithms, object-oriented design, and software-design principles.
Experience designing distributed systems, backend services, APIs, or data-intensive applications.
Experience with relational or non-relational databases and proficiency with SQL.
Experience developing, testing, deploying, and operating software in Linux and cloud environments.
Knowledge of software-development practices including source control, automated testing, code reviews, continuous integration, and continuous delivery.
Experience troubleshooting complex systems and resolving production issues.
Ability to lead technical design and execution across multiple components or workstreams.
Strong written and verbal communication skills.
Demonstrated ability to collaborate effectively with engineering, data, product, and business stakeholders.

Nice To Haves

Experience building software for cloud infrastructure, networking, capacity planning, resource planning, supply planning, or other large-scale operational domains.
Experience with cloud-native technologies, containers, Kubernetes, and service-oriented or microservice architectures.
Experience with workflow orchestration, event-driven systems, batch-processing systems, or distributed data-processing platforms.
Experience building applications that integrate machine-learning models, forecasting services, optimization methods, or analytical workflows.
Familiarity with machine-learning lifecycle concepts, including feature generation, model serving, evaluation, and monitoring.
Experience building internal enterprise applications or decision-support systems.
Experience with infrastructure-as-code, automated deployment, and configuration-management practices.
Experience with security, access control, auditability, and data-governance requirements.
Experience with OCI, AWS, Azure, or another major cloud platform.
Ability to influence technical direction across teams without direct management authority.

Responsibilities

Lead the architecture, design, and implementation of scalable services and applications supporting infrastructure planning.
Translate complex and ambiguous business and engineering requirements into well-defined technical designs and implementation plans.
Build reliable backend services, APIs, data-processing workflows, and integrations.
Develop software that incorporates analytical and machine-learning outputs into production applications and planning workflows.
Define clear interfaces and data contracts across software, data, and machine-learning components.
Design systems for scalability, availability, security, maintainability, and operational efficiency.
Establish observability through metrics, logs, traces, dashboards, and automated alerting.
Own software throughout its lifecycle, including design, development, testing, deployment, production support, and continuous improvement.
Diagnose complex production issues, perform root-cause analysis, and implement durable corrective actions.
Improve system performance, reliability, and cost efficiency through measurement and engineering analysis.
Partner with Data Scientists and Machine Learning Engineers to integrate predictive and analytical capabilities into production systems.
Collaborate with infrastructure and domain experts to ensure software behavior reflects real-world operational requirements.
Participate in architecture, design, code, and operational-readiness reviews.
Establish reusable engineering patterns, development standards, and best practices across the team.
Mentor engineers and raise the overall technical quality of the organization.
Communicate technical decisions, tradeoffs, risks, and recommendations to engineering leaders and cross-functional stakeholders.