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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.
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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.
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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.
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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.
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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.
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Multi-track delivery:
Work effectively across parallel initiatives, balance priorities, identify dependencies, and make sound technical decisions with limited supervision.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
