Software Developer 4, Salem

Job Description

Job Description - OCI Enterprise Engineering

IC4 Principal Developer - Enterprise AI Platforms Team

Description

Oracle is seeking an experienced and driven

AI Architect / Principal Developer

to join the

OCI Enterprise Engineering, Enterprise AI Platforms Team

. This team builds, integrates, deploys, and supports enterprise-grade AI platforms used across Oracle, including employee-facing generative AI experiences, agentic AI capabilities, engineering automation platforms, and secure integrations with AI providers such as OpenAI, Anthropic, OCI Generative AI, Codex-style developer assistants, and related enterprise AI services.

OCI Enterprise Engineering provides modern enterprise services to Oracle’s internal businesses and is driving improved agility, performance, availability, and security across Oracle’s enterprise and development environments. We seek passionate, highly motivated, engineering-first individuals who can help deliver scalable AI capabilities with strong enterprise controls, with particular emphasis on

Agent AI

,

AI-assisted software engineering

, and

Harness-based engineering workflows

.

As an IC4 AI Architect, you will design and deliver platform modules, integrations, agent frameworks, developer productivity capabilities, and operational services that make AI usable, secure, measurable, and productive for Oracle employees and engineering teams. You will work across multiple delivery tracks, rapidly evaluate emerging AI capabilities, and turn practical ideas into reliable enterprise deployments that can be adopted safely at Oracle scale.

This role is especially focused on building the next generation of enterprise AI platforms: autonomous and semi-autonomous agents, AI-powered engineering assistants, Codex-like coding workflows, Harness engineering automation, secure tool execution, model orchestration, observability, governance, and production-ready deployment patterns.

Responsibilities

Architecture and technical ownership:

Independently architect, design, and drive AI platform modules, provider integrations, APIs, agent runtimes, internal tools, and engineering automation services from concept through production release.

Agent AI platform development:

Design and implement enterprise-grade Agent AI capabilities, including tool-calling agents, multi-step reasoning workflows, task orchestration, memory and context management, human-in-the-loop controls, agent evaluation, and safe execution patterns.

Harness engineering integration:

Build and integrate AI capabilities into Harness-based engineering workflows, including CI/CD automation, deployment intelligence, release assistance, pipeline troubleshooting, change analysis, incident support, and developer productivity enhancements.

Codex and AI-assisted engineering workflows:

Evaluate, integrate, and operationalize Codex-style coding assistants and AI developer tools for Oracle engineering teams. Design secure workflows for code generation, code review assistance, test generation, documentation, refactoring, repository understanding, and engineering knowledge retrieval.

Enterprise AI deployment:

Design secure, scalable deployment patterns for generative AI platforms and services such as OpenAI, Anthropic, OCI Generative AI, Codex-like engineering agents, and related enterprise AI capabilities.

Multi-track delivery:

Work effectively across parallel initiatives, balance priorities, identify dependencies, and make sound technical decisions with limited supervision.

Rapid prototyping and evaluation:

Explore new AI features, model APIs, agentic workflows, RAG patterns, evaluation methods, engineering automation tools, and platform changes. Build prototypes that clarify enterprise value, risk, feasibility, and implementation approach.

Solution design and implementation:

Translate business, employee productivity, and engineering productivity needs into well-structured AI solutions, including backend services, orchestration flows, prompt and tool integrations, model routing, repository integrations, pipeline integrations, and operational controls.

Secure tool and system integration:

Design secure patterns for AI agents and coding assistants to interact with enterprise systems such as Git repositories, CI/CD platforms, Harness, Jira, Confluence, SharePoint, Outlook, observability tools, and internal APIs.

Security, privacy, and governance:

Embed enterprise security guardrails, identity and access controls, data protection practices, auditability, responsible AI controls, model risk management, prompt and code safety, and compliance requirements into platform architecture.

Operational excellence:

Design for reliability, observability, scalability, cost control, performance, supportability, incident response, and operational readiness across production AI services, agents, and engineering automation workflows.

Cross-functional collaboration:

Partner with product managers, security teams, platform engineers, application teams, developer experience teams, support teams, and business stakeholders to deliver AI capabilities that are practical, supportable, and aligned with Oracle standards.

Knowledge sharing and technical leadership:

Document design decisions, share learnings, mentor engineers, establish reusable patterns, and help the team stay current with fast-moving AI platform, agent, and AI-assisted engineering changes.

Qualifications

8+ years of experience in software engineering, cloud platforms, enterprise architecture, AI/ML engineering, developer platforms, DevOps engineering, or related technology roles.

Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or equivalent practical experience.

Proven ability to independently own architecture and delivery of complex modules, integrations, agents, automation workflows, or services in production enterprise environments.

Hands-on experience designing and building REST APIs, microservices, backend services, workflow orchestration, platform integrations, or developer productivity systems.

Practical experience with generative AI, LLMs, prompt engineering, RAG, model APIs, embeddings, vector search, agent workflows, tool calling, or AI-assisted application development.

Experience with developer engineering workflows such as CI/CD, build pipelines, deployment automation, release management, testing automation, code review, or production support.

Strong understanding of secure enterprise deployment patterns, including authentication, authorization, network controls, secrets management, audit logging, data privacy, secure code handling, and least-privilege access.

Ability to quickly learn new AI capabilities, assess tradeoffs, and recommend practical approaches for safe adoption within a large enterprise.

Strong written and verbal communication skills, with the ability to explain complex AI, agent, platform, and engineering automation concepts to technical and non-technical stakeholders.

Experience working with globally distributed teams and Agile delivery practice

Preferred Experience

Experience deploying employee-facing AI platforms, internal productivity tools, chat assistants, enterprise knowledge systems, engineering assistants, or agentic AI products.

Experience designing or integrating Agent AI systems that use tools, enterprise APIs, repository context, knowledge retrieval, workflow orchestration, or human approval flows.

Experience with Harness or comparable engineering platforms for CI/CD, deployment automation, feature delivery, release operations, governance, and developer workflows.

Experience with Codex-style AI coding tools, AI code generation, automated test generation, code review assistance, repository analysis, or AI-assisted software delivery.

Experience integrating multiple AI providers or designing abstraction layers for model routing, policy enforcement, observability, evaluation, fallback behavior, and cost controls.

Experience with content creation, document analysis, data analysis, deep research, enterprise search, software engineering workflows, or agent-based workflows in enterprise AI products.

Experience evaluating emerging AI capabilities and converting ambiguous requirements into working designs and production-ready delivery plans.

Experience defining evaluation frameworks for AI agents, coding assistants, retrieval quality, tool execution accuracy, safety, and enterprise readiness.

Technology Skills

AI platforms and model APIs:

OpenAI, Anthropic Claude, OCI Generative AI, Azure OpenAI, Codex-style coding agents, or similar enterprise AI services.

Agent AI and orchestration:

Agent workflows, function/tool calling, multi-step task execution, planning, memory and context management, human-in-the-loop patterns, agent evaluation, guardrails, and secure tool execution.

LLM application patterns:

RAG, prompt engineering, model evaluation, safety guardrails, usage analytics, model routing, provider abstraction, grounding, retrieval evaluation, and feedback loops.

AI-assisted software engineering:

Codex-style coding assistants, code generation, test generation, code review support, repository understanding, refactoring assistance, …