Oracle Cloud Infrastructure (OCI) is seeking an experienced Senior Platform Software Engineer, AI Engineering to join the AI Platform and Productivity Engineering team. In this role, you will contribute to AI innovation by designing, developing, and delivering solutions that improve employee productivity across Oracle. You will use AI development tools to accelerate software delivery and build solutions for employee support, knowledge discovery, task automation, and collaboration. You will collaborate with engineers, product managers, technical leads, and partner teams to identify employee needs, solve business problems, and turn promising AI use cases into reliable enterprise capabilities.
This role will design, develop, and maintain AI platform capabilities, including AI control plane and data plane services, Codex enablement, AI plugins and skills, GPT guidance, memory capabilities, and model routing. You will also design and develop backend services and APIs, integrate AI models with enterprise systems, and build reusable AI workflows. Working with technical leads and partner teams, you will implement testing, evaluation, and observability to ensure reliable, secure, and scalable operation.
Internal Responsibilities
Key Responsibilities
- Design, develop, deliver, and maintain secure, scalable AI software solutions that improve employee productivity across Oracle.
- Develop AI control plane and data plane services, Codex integrations, memory capabilities, and model routing components in alignment with established architecture.
- Build reusable AI plugins, skills, GPT integrations, custom applications, backend services, and APIs for enterprise use cases.
- Use Codex and other AI development tools to accelerate implementation, testing, debugging, and documentation while maintaining engineering quality.
- Collaborate with technical leads, product managers, and partner teams to clarify requirements, evaluate approaches, and deliver features from prototyping through production rollout.
- Integrate AI models with enterprise applications, data sources, and workflows, implementing runtime and middleware patterns that support interoperability and multi-tenant scale.
- Participate in architecture and code reviews, apply secure coding practices, and follow API versioning, compatibility, deprecation, and safe rollout standards.
- Implement automated testing and AI evaluations to validate functionality, accuracy, security, reliability, and performance.
- Diagnose and resolve complex issues across models, plugins, backend services, and integrations; translate employee and stakeholder feedback into product improvements.
- Implement logging, monitoring, usage telemetry, and observability, and contribute to service-level objectives, capacity planning, resilience, and operational improvements.
- Develop AI cost and usage intelligence capabilities, including cost attribution, forecasting, token budgets, threshold alerts, and reporting that supports informed investment decisions.
- Implement model selection and usage controls that balance cost, quality, risk, and performance; optimize token consumption and context through caching, context management, and workload-specific routing.
- Create technical guidance, migration guides, and adoption playbooks, and share engineering practices that make AI capabilities easier to adopt and maintain.
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 4 years of software development experience.
- 4+ years of experience designing, developing, and delivering backend services, APIs, distributed systems, developer platforms, or cloud services.
- 4+ years of programming experience in one or more languages such as Java, Python, JavaScript or Go.
- Experience developing and deploying applications on cloud platforms such as OCI, AWS, Azure, or Google Cloud.
- Experience with relational or NoSQL databases, including data modeling, querying, and integration with backend services.
- Experience with software design, API integration, automated testing, code reviews, debugging, and secure coding practices.
- Experience supporting production services through logging, monitoring, observability, incident resolution, reliability improvements, and performance optimization.
- Experience collaborating with engineering, architecture, security, product, and business stakeholders to deliver software capabilities.
Preferred Skills
AI Engineering and Platform Development
- Hands-on experience building, integrating, or operating AI-enabled applications, generative AI solutions, developer tools, or enterprise productivity applications.
- Experience using Codex or similar AI coding tools for development, testing, debugging, and documentation.
- Experience with LLM application patterns, including prompt design, retrieval-augmented generation (RAG), tool/function calling, agents, memory, evaluations, and model routing.
- Experience developing AI plugins, skills, GPT integrations, reusable workflows, or shared AI services.
- Experience developing Model Context Protocol (MCP) servers and tools to connect AI applications and agents with enterprise APIs, backend services, and data sources, including authentication, authorization, testing, and observability.
- Familiarity with AI control plane and data plane concepts and integrating AI capabilities with backend services, APIs, and enterprise systems.
- Experience implementing AI security, privacy, model-access controls, and policy-based model selection.
- Experience with AI cost optimization, including token and context management, usage telemetry, cost attribution, forecasting, budgeting, and caching.
External Responsibilities
Key Responsibilities
- Design, develop, deliver, and maintain secure, scalable AI software solutions that improve employee productivity across Oracle.
- Develop AI control plane and data plane services, Codex integrations, memory capabilities, and model routing components in alignment with established architecture.
- Build reusable AI plugins, skills, GPT integrations, custom applications, backend services, and APIs for enterprise use cases.
- Use Codex and other AI development tools to accelerate implementation, testing, debugging, and documentation while maintaining engineering quality.
- Collaborate with technical leads, product managers, and partner teams to clarify requirements, evaluate approaches, and deliver features from prototyping through production rollout.
- Integrate AI models with enterprise applications, data sources, and workflows, implementing runtime and middleware patterns that support interoperability and multi-tenant scale.
- Participate in architecture and code reviews, apply secure coding practices, and follow API versioning, compatibility, deprecation, and safe rollout standards.
- Implement automated testing and AI evaluations to validate functionality, accuracy, security, reliability, and performance.
- Diagnose and resolve complex issues across models, plugins, backend services, and integrations; translate employee and stakeholder feedback into product improvements.
- Implement logging, monitoring, usage telemetry, and observability, and contribute to service-level objectives, capacity planning, resilience, and operational improvements.
- Develop AI cost and usage intelligence capabilities, including cost attribution, forecasting, token budgets, threshold alerts, and reporting that supports informed investment decisions.
- Implement model selection and usage controls that balance cost, quality, risk, and performance; optimize token consumption and context through caching, context management, and workload-specific routing.
- Create technical guidance, migration guides, and adoption playbooks, and share engineering practices that make AI capabilities easier to adopt and maintain.
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 4 years of software development experience.
- 4+ years of experience designing, developing, and delivering backend services, APIs, distributed systems, developer platforms, or cloud services.
- 4+ years of programming experience in one or more languages such as Java, Python, JavaScript or Go.
- Experience developing and deploying applications on cloud platforms such as OCI, AWS, Azure, or Google Cloud.
- Experience with relational or NoSQL databases, including data modeling, querying, and integration with backend services.
- Experience with software design, API integration, automated testing, code reviews, debugging, and secure coding practices.
- Experience supporting production services through logging, monitoring, observability, incident resolution, reliability improvements, and performance optimization.
- Experience collaborating with engineering, architecture, security, product, and business stakeholders to deliver software capabilities.
Preferred Skills
AI Engineering and Platform Development
- Hands-on experience building, integrating, or operating AI-enabled applications, generative AI solutions, developer tools, or enterprise productivity applications.
- Experience using Codex or similar AI coding tools for development, testing, debugging, and documentation.
- Experience with LLM application patterns, including prompt design, retrieval-augmented generation (RAG), tool/function calling, agents, memory, evaluations, and model routing.
- Experience developing AI plugins, skills, GPT integrations, reusable workflows, or shared AI services.
- Experience developing Model Context Protocol (MCP) servers and tools to connect AI applications and agents with enterprise APIs, backend services, and data sources, including authentication, authorization, testing, and observability.
- Familiarity with AI control plane and data plane concepts and integrating AI capabilities with backend services, APIs, and enterprise systems.
- Experience implementing AI security, privacy, model-access controls, and policy-based model selection.
- Experience with AI cost optimization, including token and context management, usage telemetry, cost attribution, forecasting, budgeting, and caching.
