AI Ops Engineer — AI Workflow Automator

Oracle Corporation; Nashville (TN); On-site; USD 120,000 - 180,000; Full time

Job summary

Oracle Corporation seeks an AI Ops Engineer to advance the AI Acceleration initiative, building AI-enabled workflows and internal tools for Oracle business units and turning business needs into structured, testable solutions.

Role involves designing practical AI adoption paths, working with LLMs and related artifacts, and ensuring reliable, scalable automation through disciplined technical judgment and clear handoffs.

Qualifications

• Strong programming and CS fundamentals, including data structures, algorithms, and dynamic programming.
• Working knowledge of ML and LLM applications, linear algebra, matrices and tensors, visualization, numerical computation, and model evaluation.
• Ability to structure ambiguous work, manage priorities, communicate clearly, and apply sound judgment when using AI tools.

Responsibilities

• Build state-aware AI automations, scripts, internal tools, and workflow components that preserve context and support clear handoffs.
• Turn SOPs into usable handoffs, documentation, and AI-enabled workflow designs; prototype practical use cases for everyday work.
• Work with LLMs and related artifacts—prompts, system instructions, evaluations, retrieval configurations, knowledge sources, and outputs—while validating quality and reliability.
• Consult directly with Oracle business units, using adoption metrics and personalized conversations to guide change and improve outcomes.

Skills

AI/ML fundamentals

Programming

Communication

Education

Degree in AI/ML/CS/Data Science or related discipline

Tools

Git

Cloud platforms

REST APIs

DAG workflows

Job description

Oracle Corporation seeks an AI Ops Engineer to advance the AI Acceleration initiative, building AI-enabled workflows and internal tools for Oracle business units and turning business needs into structured, testable solutions.

Role involves designing practical AI adoption paths, working with LLMs and related artifacts, and ensuring reliable, scalable automation through disciplined technical judgment and clear handoffs.