Note: The job is a remote job and is open to candidates in USA. The hiring organization is seeking an AI Ops Engineer II to help build and scale an enterprise operational AI platform. The role focuses on developing multi-agent AI workflows, integrating infrastructure and observability systems, and delivering AI-driven capabilities for Help Desk automation, incident triage, root cause analysis, and self-healing operations.
Responsibilities
- Assist in the architecture and development of the unified AI assistant, ensuring a seamless experience for I&O engineers
- Develop and register AI agents and Model Context Protocol (MCP) integrations to handle real-time, interactive operational tasks
- Build backend workflows that support discovery, platform engineering sizing decisions, and proactive outage prevention
- Develop robust data integrations connecting the AI platform to foundational infrastructure sources, including CMDB, GCP, ServiceNow, Zabbix, and Dynatrace
- Automate the ingestion and contextualization of infrastructure data across the entire I&O organization, leveraging AI-assisted development tools like Devin and Windsurf to accelerate engineering and accurately perform reactive and scheduled operational tasks
- Collaborate with observability and enterprise search (e.g., Glean) teams to cross-validate data, manage knowledge quality, and prevent LLM hallucinations
- Deliver initial "low-hanging fruit" use cases, specifically focusing on Help Desk automation and Incident Management (MI) support
- Design AI capabilities to assist with change windows, maintenance suppression, and coordination of self-healing actions
- Establish AI observability metrics to measure confidence levels, token usage, and improvements to Mean Time to Resolution (MTTR)
Skills
- Candidates are expected to have 1 to 2 years of direct, hands-on experience with modern LLMs, multi-agent frameworks, and AI coding assistants, built on top of a solid 2 to 3-year foundational background in backend software engineering (Python), API integration, data engineering, or traditional AIOps
- Deep understanding of Large Language Models (LLMs), advanced prompt engineering, and Retrieval-Augmented Generation (RAG)
- Hands-on experience or familiarity with advanced AI coding assistants and autonomous engineering platforms such as Devin and Windsurf
- Hands-on expertise
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