Principal Application Software Engineer

Oracle Cloud Infrastructure (OCI) is seeking an experienced Principal Platform Software Engineer to provide technical leadership within the AI Platform and Productivity Engineering team. You will drive AI innovation by architecting, developing, and delivering platform capabilities and applications that improve employee productivity across Oracle.

This role will lead the technical design and implementation of AI control plane and data plane services, Codex enablement, AI plugins and skills, Model Context Protocol (MCP) servers and tools, GPT integrations, memory capabilities, and model routing. You will design and develop backend services and APIs, integrate AI models with enterprise systems, and establish reusable patterns for employee support, knowledge discovery, task automation, and collaboration.

Working with engineers, product managers, security teams, and partner organizations, you will translate complex requirements into practical technical solutions and guide projects from prototyping through production operation. You will establish architecture and engineering standards, lead design reviews, resolve complex technical issues, and mentor engineers while remaining actively involved in implementation.

Lead the design of AI cost, token, and usage intelligence capabilities, including telemetry, cost attribution, forecasting, token budgets, threshold alerts, and model-cost optimization. These capabilities will help teams evaluate AI investment and scale adoption safely and efficiently.

Key Responsibilities

• Lead the architecture, development, integration, and operation of secure, scalable AI platforms, enterprise applications, backend services, and APIs.
• Drive technical delivery across multiple teams, clarify requirements, evaluate architectural tradeoffs, and resolve dependencies and technical blockers.
• Define and implement AI control plane and data plane architecture, Codex integrations, memory capabilities, and model routing.
• Architect and develop reusable AI plugins, skills, GPT integrations, MCP servers and tools, and enterprise AI workflows.
• Establish technical guidance for integrating AI models with enterprise applications, data sources, and services, including authentication, authorization, and access controls.
• Use AI development tools to accelerate engineering delivery and establish practices for validating generated code and maintaining software quality.
• Lead architecture and code reviews, contribute to complex implementations, and mentor engineers on design, debugging, testing, and operational practices.
• Define runtime, middleware, and API standards that support interoperability and multi-tenant scale, including versioning, compatibility, deprecation, and safe rollout practices.
• Establish automated testing and AI evaluation approaches that assess functionality, output quality, security, reliability, latency, and cost.
• Diagnose and resolve complex cross-service issues, guide performance optimization, and translate employee and stakeholder feedback into product improvements.
• Drive shared observability, service-level objectives, capacity planning, resilience, and operational improvements across AI workloads.
• Lead the design and delivery of AI usage telemetry, cost attribution, forecasting, token budgets, threshold alerts, and reporting that supports informed investment decisions.
• Define and implement model selection and usage controls that balance cost, quality, risk, and performance.
• Establish token and context optimization patterns, including caching, context management, and workload-specific routing.
Create architectural guidance, migration plans, and adoption playbooks that improve platform consistency and make shared AI capabilities easier to adopt.

Minimum Job Qualifications Education and Experience:

• Bachelor of Technology (B.Tech) or another bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Electrical/Electronics Engineering, Computer Information Systems, Information Systems, Information Technology, Telecommunications, Mathematics, Physics, or a related field, and at least 7 years of software development experience.

• 7+ years of experience designing and delivering scalable distributed systems, backend services, APIs, developer platforms, or cloud services.

• 7+ years of experience developing or operating applications on cloud platforms such as OCI, AWS, Azure, or Google Cloud.

• 7+ years of programming experience in one or more languages such as Java, Python, JavaScript or Go.

• 7+ years of experience with relational or NoSQL databases, including data modeling, querying, and integration with backend services.

• Experience leading technical design and delivery across teams, reviewing architecture, and mentoring engineers.

• Experience with API design and integration, automated testing, secure coding, complex debugging, and performance optimization.

• Experience operating production services, including observability, logging, monitoring, incident response, service reliability, and capacity planning.

• Experience collaborating with engineering, architecture, security, product, and business stakeholders to deliver shared platform capabilities.

Preferred Skills: AI Engineering and Platform Development

• Experience architecting, developing, or operating AI-enabled applications, generative AI platforms, developer tools, or enterprise productivity solutions.
• Experience using Codex or similar AI coding tools and establishing engineering practices for their effective use.
• Experience with LLM application patterns, including prompt design, retrieval-augmented generation (RAG), tool/function calling, agents, memory, evaluations, and model routing.
• Experience designing AI control plane and data plane services, reusable plugins, skills, GPT integrations, and enterprise AI enablement platforms.
• Experience designing and developing MCP servers and tools that connect AI applications and agents with enterprise APIs, backend services, and data sources, including authentication, authorization, testing, and observability.
• Experience implementing AI governance, security, privacy, model-access controls, and policy-based model selection.
• Experience establishing AI evaluation frameworks and production monitoring for quality, task completion, reliability, latency, and cost.
• Experience with AI cost optimization, including token and context management, usage telemetry, cost attribution, forecasting, budgeting, caching, and model-performance optimization.