AI Automation Engineer

Job Description

The AI Automation Engineer will develop AI‑assisted automation, integrations, scripts, workflows, and reusable components that improve engineering productivity, testing, documentation, observability, incident response, and operational efficiency across The Mutual Group and its member insurance carriers. This is a hands‑on engineering role for a practical builder who can use AI, automation, APIs, data, and modern engineering tools to simplify work, reduce manual effort, and improve the speed and quality of technology delivery.

Information Technology

• Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in‑office schedule. The initial training period may require additional in‑office days.

• Design, build, test, and maintain AI‑assisted automation components, scripts, integrations, workflows, and reusable engineering assets.
• Develop automation that improves software delivery, testing, documentation, release readiness, operational workflows, and employee productivity.
• Translate technical requirements and use cases into working solutions using APIs, scripts, cloud services, workflow tools, and AI‑enabled development platforms.
• Create reusable templates, connectors, prompts, scripts, and implementation examples that can be adopted by other IT teams.
• Support proof‑of‑concept development and help mature successful automation patterns into repeatable, production‑ready capabilities.

• Build AI‑enabled workflows that support coding, test generation, documentation, requirements analysis, code review, knowledge retrieval, and developer productivity.
• Configure and support productivity use cases using tools such as ChatGPT, Microsoft Copilot, and related AI assistants.
• Develop practical automation for summarization, classification, document processing, ticket analysis, workflow routing, meeting support, and knowledge assistance.
• Partner with engineering and operations teams to identify repetitive work that can be simplified through AI‑enabled automation.
• Document usage patterns, reusable prompts, workflows, and enablement materials that help teams adopt AI tools effectively and responsibly.

• Integrate automation capabilities with enterprise applications, APIs, data sources, document repositories, service management platforms, collaboration tools, and cloud services.
• Support AI solution development using Generative AI patterns such as LLMs, embeddings, prompt engineering, retrieval‑augmented generation, semantic search, and enterprise knowledge integration.
• Assist with Agentic AI patterns, including tool and function calling, workflow orchestration, human‑in‑the‑loop controls, guardrails, monitoring, and safe execution.
• Use Model Context Protocol (MCP) or similar approaches to connect AI systems with enterprise tools, APIs, data sources, and workflow actions in a secure and governed manner.
• Contribute to reusable components for prompt handling, response validation, logging, monitoring, evaluation, and production support.

• Build automation that supports observability, incident summarization, root cause analysis, alert enrichment, runbook automation, service management, and operational productivity.
• Partner with Infrastructure and IT Operations teams to identify opportunities for predictive monitoring, automated remediation, knowledge retrieval, and workflow simplification.
• Support integration with monitoring, logging, ticketing, collaboration, and service management tools.
• Create operational runbooks, support documentation, and repeatable workflows for AI‑enabled operations use cases.
• Help measure improvements in manual effort reduction, cycle time, documentation quality, incident response, operational efficiency, and reuse.

• Apply secure‑by‑design and privacy‑by‑design practices in all automation and AI‑enabled workflows.
• Follow enterprise standards for identity and access management, sensitive data handling, logging, monitoring, output validation, and responsible AI…