AI Ops Engineer

About the Role

AI Acceleration is a product research initiative building practical AI capabilities for Oracle business units. The initiative aims to research, prototype, and scale useful AI capabilities. Its work spans two areas: state-aware automation for complex workflows and AI enablement for everyday tasks.

Key Responsibilities

• Contribute to a product research initiative that builds and scales AI-enabled workflows and internal tools for Oracle business units.
• Turn internal business needs into structured, testable solutions that improve how work gets done.
• Create a practical, responsible path for AI adoption through visible, well-organized delivery.
• Bring clear, creative thinking, strong problem framing, and disciplined technical judgment.
• Build state-aware AI automations, scripts, internal tools, and workflow components that preserve context and support clear handoffs.
• Turn standard operating procedures into usable handoffs, documentation, and AI-enabled workflow designs; prototype practical use cases for everyday work.
• Work fluently with LLMs and their artifacts – prompts, system instructions, evaluations, retrieval configurations, knowledge sources, and generated outputs – while validating quality and reliability.
• Consult directly with Oracle business units, using adoption metrics and personalized conversations to guide change and improve outcomes.

Requirements & Tech Stack

• Strong programming and computer science foundations, 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.

Qualifications

• Degree in AI, machine learning, computer science, data science, philosophy, or a related discipline, or equivalent practical experience.

Preferred Qualifications

• Experience with Git, cloud platforms, databases, REST APIs, MCPs, command-line tools, and LLM application development.
• Experience with GPU-based computation, directed acyclic graph workflows, development operations, or AI workflow automation.
• Experience with repository and artifact management, documentation systems, agile delivery, or high-volume work management.
• Experience translating processes into handoffs, supporting technology adoption, or advising internal business stakeholders.