AI Automation Engineer

Job Title: AI Automation Engineer

Location: (REMOTE)

Duration : Long Term

Job Description

• 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.
• Required to securely and reliably integrate these tools into complex, enterprise-grade infrastructure platforms like ServiceNow, Dynatrace, and Google Cloud Platform.
• Their initial focus will be designing robust data integrations that connect our LLM framework to essential infrastructure systems like ServiceNow, Dynatrace, Zabbix, and Google Cloud Platform. Using tools like the Model Context Protocol (MCP) and AI-assisted development (Devin/Windsurf)

Operational AI Interface Development

• 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.

Foundational Data & Integration

• Develop robust data integrations connecting the AI platform to foundational infrastructure sources, including CMDB, Google Cloud Platform, 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.

Technical Proficiencies:

• AI & LLM Development: Deep understanding of Large Language Models (LLMs), advanced prompt engineering, and Retrieval-Augmented Generation (RAG).
• AI Developer Tools: Hands-on experience or familiarity with advanced AI coding assistants and autonomous engineering platforms such as Devin and Windsurf.
• Agentic Frameworks: Hands-on experience building multi-agent AI systems (e.g., LangChain, AutoGen, CrewAI) and utilizing Model Context Protocols (MCP) or tool-calling architectures.
• Programming & APIs: Strong software engineering skills, primarily in Python, with extensive experience building and consuming RESTful APIs to connect disparate systems.
• AIOps & Infrastructure Data: Familiarity with broader I&O data, including ITSM platforms (ServiceNow for ticketing/CMDB) and monitoring/telemetry systems (Dynatrace, Zabbix, Google Cloud Platform).
• Data Quality & Governance: Experience handling unstructured and structured knowledge bases, improving data quality, and implementing guardrails for AI safety and accuracy.